The importance of the patients point of view on their health status is widely recognised. Patient-reported outcomes is a broad term encompassing a large variety of different health data reported by patients, as symptoms, functional status, Quality of Life and Health-Related Quality of Life. Measurements of Health-Related Quality of Life have been developed during many years of researches, and a lot of validated questionnaires exist. However, few attempts have been made to standardise the evaluation of instruments characteristics, no recommendations are made about interpretation on Health-Related Quality of Life results, especially regarding the clinical significance of a change leading a therapeutic approach. Moreover, the true value of Health-Related Quality of Life evaluations in clinical trials has not yet been completely defined. An important step towards a more structured and frequent use of Patient-Reported Outcomes in drug development is represented by the FDA Guidance, issued on February 2006. In our paper we aim to report some considerations on this Guidance. Our comments focus especially on the characteristics of instruments to use, the Minimal Important Difference, and the methods to calculate it. Furthermore, we present the advantages and opportunities of using the Patient-Reported Outcomes in drug development, as seen by a pharmaceutical company. The Patient-Reported Outcomes can provide additional data to make a drug more competitive than others of the same pharmacological class, and a well demonstrated positive impact on the patient' health status and daily life might allow a higher price and/or the inclusion in a reimbursement list. Applying extensively the FDA Guidance in the next trials could lead to a wider culture of subjective measurement, and to a greater consideration for the patient's opinions on his/her care. Moreover, prescribing doctors and payers could benefit from subjective information to better define the value of drugs.

BACKGROUND: The tryptophan hydroxylase 1 (TPH1) gene is reported to be associated with suicidal behavior. This has not been confirmed by prospective studies of suicide and clinical or biological mediators of this genetic risk have not been identified. METHODS: 343 subjects (Caucasian, African-American, Hispanic) presenting with a Major Depressive Episode were genotyped for polymorphisms A218C in intron 7 and A-6526G in the promoter region of TPH1, and monitored for suicide attempts for up to one year. Clinical correlates of suicidal behavior and CSF-HIAA, HVA and MHPG levels were explored as possible mediators of genetic risk. Analyses were adjusted for ethnicity. RESULTS: The AA genotype on intron 7 and the AA genotype on the promoter (both more prevalent in Caucasians) predicted suicide attempts during the 1 year follow-up, and were associated with past attempts of high medical lethality, regardless of ethnicity. The intron 7 genotype was associated with fewer reported reasons for living, and lower impulsivity. Haplotype analysis indicated significant increase in risk of suicide attempts for subjects with four risk alleles. TPH1 genotype was not associated with CSF metabolite levels. LIMITATIONS: The TPH1 gene is likely one of several genes associated with suicidal behavior. Power to detect differential genotype effects by ethnicity is low. CONCLUSIONS: Polymorphisms of TPH1 may assist in identifying a subgroup of mood disorder patients that is at higher risk for suicidal behavior.

The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood approach and forces sparsity by using a lasso-type penalty. We establish a rate of con- vergence in the Frobenius norm as both data dimension p and sample size n are allowed to grow, and show that the rate depends explicitly on how sparse the true concentration matrix is. We also show that a correlation- based version of the method exhibits better rates in the operator norm. We also derive a fast iterative algorithm for computing the estimator, which relies on the popular Cholesky decomposition of the inverse but produces a permutation-invariant estimator. The method is compared to other es- timators on simulated data and on a real data example of tumor tissue classification using gene expression data.

This work examines the association between personality dimensions (extraversion and neuroticism) and subjective well-being. Subjective well-being is associated both with extraversion and neuroticism, and currently, neuroticism is generally considered the more important. A total of 368 students from the University of Rovira i Virgili completed the Extraversion and Neuroticism subscales of the revised Eysenck Personality Questionnaire (Eysenck, Eysenck, and Barrett, 1985), the Satisfaction with Life Scale (SWLS; Diener, Emmons, Larsen, and Griffin, 1985), and the Positive and Negative Affect Scale (Watson, Clark, and Tellegen, 1988). Regression analyses revealed the personality variable of neuroticism as one of the most important correlates of subjective well-being. Regression analyses also showed that 44% of the variance of subjective well-being was accounted for by neuroticism, whereas extraversion only explained 8% of the variance.

BACKGROUND: Data about quality of life (QoL) are important to estimate the impact of diseases on functioning and well-being. The present study was designed to assess the association of different aspects of panic disorder (PD) with QoL and to examine the relationship between QoL and symptomatic outcome following brief cognitive-behavioral group therapy (CBGT). METHOD: The sample consisted of 55 consecutively recruited outpatients suffering from PD who underwent CBGT. QoL was assessed by the Medical Outcomes Study 36-item Short-Form Health Survey (SF-36) at baseline, post-treatment and six months follow-up. SF-36 baseline scores were compared with normative data obtained from a large German population sample. RESULTS: Agoraphobia, disability, and worries about health were significantly associated with decreased QoL, whereas frequency, severity and duration of panic attacks were not. Treatment responders showed significantly better QoL than non-responders. PD symptom reduction following CBGT was associated with considerable improvement in emotional and physical aspects of QoL. However, the vitality subscale of the SF-36 remained largely unchanged over time. CONCLUSIONS: Our results are encouraging for cognitive-behavior therapists who treat patients suffering from PD in groups, since decrease of PD symptoms appears to be associated with considerable improvements in QoL. Nevertheless, additional interventions designed to target specific aspects of QoL, in particular vitality, may be useful to enhance patients' well-being.

BACKGROUND: Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies. RESULTS: The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy. CONCLUSION: GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.

OBJECTIVE: The Empathy Quotient (EQ) is a self-report that was developed to measure the cognitive and affective aspects of empathy. We further evaluated its validity in 2 studies. METHOD: The psychometric qualities of the French version of the EQ, and its correspondence with 2 other measures of empathy (Interpersonal Reactivity Index and the Empathy Scale of the Impulsiveness-Venturesomeness-Empathy Questionnaire), and with dimensions of the emotional state (depression and anxiety), were evaluated in a sample of 410 students (201 men and 209 women). Second, the clinical validity of the EQ was investigated in participants expected to have dysfunctional empathy. For this purpose, EQ scores of 16 people with autistic spectrum disorder (ASD) were collected. RESULTS: The EQ showed satisfying internal, convergent, test-retest and discriminant validity. The confirmatory factorial analyses suggested a 3-factor structure offered a good fit to the data. The women's superiority in empathy was replicated. As expected, the ASD EQ scores were very low. CONCLUSION: This study provides further evidence that the EQ is reliable in this population and should be recommended to estimate empathy problems, notably in individuals with troubled interpersonal interaction patterns.

Random forests are one of the most popular statistical learning algorithms, and a variety of methods for fitting random forests and related recursive partitioning approaches is available in R. This paper points out two impor- tant features of the random forest implementa- tion cforest available in the party package: The resulting forests are unbiased and thus prefer- able to the randomForest implementation avail- able in randomForest if predictor variables are of different types. Moreover, a conditional per- mutation importance measure has recently been added to the party package, which can help eval- uate the importance of correlated predictor vari- ables. The rationale of this new measure is illus- trated and hands-on advice is given for the usage of recursive partitioning tools in R.

BACKGROUND: No validated disease-specific measures are available to assess health-related quality of life (HRQoL) in adult subjects with immune thrombocytopenic purpura (ITP). Therefore, we sought to develop and validate the ITP-Patient Assessment Questionnaire (ITP-PAQ) for adult subjects with ITP. METHODS: Information from literature reviews, focus groups with subjects, and clinicians were used to develop 50 ITP-PAQ items. Factor analyses were conducted to develop the scale structure and reduce the number of items. The final 44-item ITP-PAQ, which includes ten scales [Symptoms (S), Bother-Physical Health (B), Fatigue/Sleep (FT), Activity (A), Fear (FR), Psychological Health (PH), Work (W), Social Activity (SA), Women's Reproductive Health (RH), and Overall (QoL)], was self-administered to adult ITP subjects at baseline and 7-10 days later. Test-retest reliability, internal consistency reliability, construct and known groups validity of the final ITP-PAQ were evaluated. RESULTS: Seventy-three subjects with ITP completed the questionnaire twice. Test-retest reliability, as measured by the intra-class correlation, ranged from 0.52-0.90. Internal consistency reliability was demonstrated with Cronbach's alpha for all scales above the acceptable level of 0.70 (range: 0.71-0.92), except for RH (0.66). Construct validity, assessed by correlating ITP-PAQ scales with established measures (Short Form-36 v.1, SF-36 and Center for Epidemiologic Studies Depression Scale, CES-D), was demonstrated through moderate correlations between the ITP-PAQ SA and SF-36 Social Function scales (r = 0.67), and between ITP-PAQ PH and SF-36 Mental Health Scales (r = 0.63). Moderate to strong inter-scale correlations were reported between ITP-PAQ scales and the CES-D, except for the RH scale. Known groups validity was evaluated by comparing mean scores for groups that differed clinically. Statistically significant differences (p < 0.01) were observed when subjects were categorized by treatment status [S, FT, B, A, PH, and QoL, perceived effectiveness of ITP treatment [S], and time elapsed since ITP diagnosis [PH]. CONCLUSION: Results provide preliminary evidence of the reliability and validity of the ITP-PAQ in adult subjects with ITP. Further work should be conducted to assess the responsiveness and to estimate the minimal clinical important difference of the ITP-PAQ to more fully understand the impact of ITP and its treatments on HRQoL.

Imaging genetics provides a unique tool with which to explore and evaluate the functional impact of brain-relevant genetic polymorphisms with the potential to understand their impact on behavior. Because statistical association with clinical diagnosis does not establish biological significance nor identify a mechanism of risk, imaging genetics is a uniquely valuable strategy for extending statistical evidence with biological data. Applications include identifying biologic mechanisms and pathways that mediate individual differences in complex behaviors and vulnerability to disease, and conversely identifying genes that contribute to functional variation in brain circuitry. Additionally, neuroimaging genetics can validate data that suggest an association with psychiatric illness as well as providing evidence of the mechanism of risk. This review also outlines several critical principles of imaging genetics including a rational approach to the selection of candidate genes, the selection of task paradigms that could be plausibly linked to the biology of the gene of interest, and careful control of non-genetic factors. The future of imaging genetics holds great promise for brain research and for biologic validation of genetic validation in CNS disorders, but a disciplined application of the basic principles outlined in this review is critical.

Abstract. tion of relevant variables. Variable selection suffers from instability and the power to detect relevant variables is typically low if predictor variables are highly correlated. When taking the multiplicity of the testing problem into account, the power diminishes even further. To gain power and insight, it can be advantageous to look for influence not at the level of individual variables but rather at the level of clusters of highly correlated variables. We propose a hierarchical approach. Variable importance is first tested at the coarsest level, corresponding to the global null hypothesis. If possible, the method tries then to attribute any effect to smaller sub-clusters or even individual variables. The smallest possible clusters which still exhibit a significant influence on the response variable are retained. It is shown that the proposed testing procedure controls the family-wise error rate at a pre- specified level, simultaneously over all resolution levels. The method has comparable power to Bonferroni-Holm on the level of individual variables and dramatically larger power for coarser resolution levels. The best resolution level is selected adaptively.

In high-throughput studies involving genetic data such as from gene expression microarrays, differential expression analysis between two or more experimental conditions has been a very common analytical task. Much of the resulting literature on multiple comparisons has paid relatively little attention to the choice of test statistic. In this article, we focus on the issue of choice of test statistic based on a special pattern of differential expression. The approach here is based on recasting multiple comparisons procedures for assessing outlying expression values. A major complication is that the resulting p-values are discrete; some theoretical properties of sequential testing procedures in this context are explored. We propose the use of q-value estimation procedures in this setting. Data from a gene expression profiling experiment in prostate cancer are used to illustrate the methodology.

We propose the use of a new false discovery rate (FDR) controlling procedure as a model selection penalized method, and compare its performance to that of other penalized methods over a wide range of realistic settings: nonorthogonal design matrices, moderate and large pool of explanatory variables, and both sparse and nonsparse models, in the sense that they may include a small and large fraction of the potential variables (and even all). The comparison is done by a comprehensive simulation study, using a quantitative framework for performance comparisons in the form of empirical minimaxity relative to a "random oracle": the oracle model selection performance on data dependent forward selected family of potential models. We show that FDR based procedures have good performance, and in particular the newly proposed method, emerges as having empirical minimax performance. Interestingly, using FDR level of 0.05 is a global best.

Standard item response theory (IRT) models fit to dichotomous examination responses ignore the fact that sets of items (testlets) often come from a single common stimuli (e.g. a reading comprehension passage). In this setting, all items given to an examinee are unlikely to be conditionally independent (given examinee proficiency). Models that assume conditional independence will overestimate the precision with which examinee proficiency is measured. Overstatement of precision may lead to inaccurate inferences such as prematurely ending an examination in which the stopping rule is based on the estimated standard error of examinee proficiency (e.g., an adaptive test). To model examinations that may be a mixture of independent items and testlets, we modified one standard IRT model to include an additional random effect for items nested within the same testlet. We use a Bayesian framework to facilitate posterior inference via a Data Augmented Gibbs Sampler (DAGS; Tanner & Wong, 1987). The modified and standard IRT models are both applied to a data set from a disclosed form of the SAT. We also provide simulation results that indicates that the degree of precision bias is a function of the variability of the testlet effects, as well as the testlet design.

Seven experts on personality measurement here discuss the viability of public-domain personality measures, focusing on the International Personality Item Pool (IPIP) as a prototype. Since its incep- tion in 1996, the use of items and scales from the IPIP has increased dramatically. Items from the IPIP have been translated from English into more than 25 other languages. Currently over 80 publications using IPIP scales are listed at the IPIP Web site (http://ipip.ori.org), and the rate of IPIP- related publications has been increasing rapidly. The growing popularity of the IPIP can be attrib- uted to Wve factors: (1) It is cost free; (2) its items can be obtained instantaneously via the Internet; (3) it includes over 2000 items, all easily available for inspection; (4) scoring keys for IPIP scales are provided; and (5) its items can be presented in any order, interspersed with other items, reworded, translated into other languages, and administered on the World Wide Web without asking permis- sion of anyone. The unrestricted availability of the IPIP raises concerns about possible misuse by unqualiWed persons, and the freedom of researchers to use the IPIP in idiosyncratic ways raises the possibility of fragmentation rather than scientiWc uniWcation in personality research.

A great deal of prior research using structural equation models has focused on longitudinal analyses and biometric analyses. Some of this research has even considered the simultaneous analysis of both kinds of analytic problems. The key benefits of these kinds of analyses come from the estimation of novel parameters, such as the heritability of changes. This paper discusses some recent extensions of longitudinal multivariate models that can be informative within biometric designs. In the methods section we review a previous latent growth structural equation analysis of the New York Twin (NYT) longitudinal data (from McArdle et al., 1998). In the models section we recast this growth model in terms of latent difference scores, add several new dynamic components, including coupling parameters, and consider biometric components and examine model stability. In the results section we present new univariate and bivariate dynamic estimates and tests of various dynamic hypotheses for the NYT data, and we consider a few ways to interpret the age-related biometric components of these models. In the discussion we consider our limitations and present suggestions for future dynamic-genetic research.

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependence between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

BACKGROUND: Comorbidity of certain obsessive-compulsive spectrum disorders (OCSDs; such as Tourette's disorder) in obsessive-compulsive disorder (OCD) may serve to define important OCD subtypes characterized by differing phenomenology and neurobiological mechanisms. Comorbidity of the putative OCSDs in OCD has, however, not often been systematically investigated. METHODS: The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition , Axis I Disorders-Patient Version as well as a Structured Clinical Interview for Putative OCSDs (SCID-OCSD) were administered to 210 adult patients with OCD (N = 210, 102 men and 108 women; mean age, 35.7 +/- 13.3). A subset of Caucasian subjects (with OCD, n = 171; control subjects, n = 168), including subjects from the genetically homogeneous Afrikaner population (with OCD, n = 77; control subjects, n = 144), was genotyped for polymorphisms in genes involved in monoamine function. Because the items of the SCID-OCSD are binary (present/absent), a cluster analysis (Ward's method) using the items of SCID-OCSD was conducted. The association of identified clusters with demographic variables (age, gender), clinical variables (age of onset, obsessive-compulsive symptom severity and dimensions, level of insight, temperament/character, treatment response), and monoaminergic genotypes was examined. RESULTS: Cluster analysis of the OCSDs in our sample of patients with OCD identified 3 separate clusters at a 1.1 linkage distance level. The 3 clusters were named as follows: (1) "reward deficiency" (including trichotillomania, Tourette's disorder, pathological gambling, and hypersexual disorder), (2) "impulsivity" (including compulsive shopping, kleptomania, eating disorders, self-injury, and intermittent explosive disorder), and (3) "somatic" (including body dysmorphic disorder and hypochondriasis). Several significant associations were found between cluster scores and other variables; for example, cluster I scores were associated with earlier age of onset of OCD and the presence of tics, cluster II scores were associated with female gender and childhood emotional abuse, and cluster III scores were associated with less insight and with somatic obsessions and compulsions. However, none of these clusters were associated with any particular genetic variant. CONCLUSION: Analysis of comorbid OCSDs in OCD suggested that these lie on a number of different dimensions. These dimensions are partially consistent with previous theoretical approaches taken toward classifying OCD spectrum disorders. The lack of genetic validation of these clusters in the present study may indicate the involvement of other, as yet untested, genes. Further genetic and cluster analyses of comorbid OCSDs in OCD may ultimately contribute to a better delineation of OCD endophenotypes.

Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques and sometimes mistaken as the same statistical method. However, there are distinct differences between PCA and EFA. Similarities and differences between PCA and EFA will be examined. Examples of PCA and EFA with PRINCOMP and FACTOR will be illustrated and discussed.

Mokken scale analysis (MSA) can be used to assess and build unidimensional scales from an item pool that is sensitive to multiple dimensions. These scales satisfy a set of scaling conditions, one of which follows from the model of monotone homogeneity. An important drawback of the MSA program is that the sequential item selection and scale construction procedure may not find the dominant underlying dimensionality of the responses to a set of items. The authors investigated alternative hierarchical item selection procedures and compared the performance of four hierarchical methods and the sequential clustering method in the MSA context. The results showed that hierarchical clustering methods can improve the search process of the dominant dimensionality of a data matrix. In particular, the complete linkage and scale linkage methods were promising in finding the dimensionality of the item response data from a set of items.

Factors that confer predisposition and vulnerability for alcoholism and other substance abuse disorders may be described usefully within the gene-environment interplay framework. Thus, it is postulated that heritability provides a major contribution not only to alcohol but also to other substances of abuse. Studies of evoked potential amplitude reduction have provided a highly suitable and testable method for the assessment of both environmentally-determined and heritable characteristics pertaining to substance use and dependence. The different personal attributes that may co-exist with parental influence or exist in a shared, monozygotic relationship contribute to the final expression of addiction. In this connection, it appears that personality disorders are highly prevalent co-morbid conditions among addicted individuals, and, this co-morbidity is likely to be accounted for by multiple complex etiological relationships, not least in adolescent individuals. Co-morbidity associated with deficient executive functioning may be observed too in alcohol-related aggressiveness and crimes of violence. The successful intervention into alcohol dependence and craving brought about by baclofen in both human and animal studies elucidates glutamatergic mechanisms in alcoholism whereas the role of the dopamine transporter, in conjunction with both the noradrenergic and serotonergic transporters, are implicated in cocaine dependence and craving. The role of the cannabinoids in ontogeny through an influence upon the expression of key genes for the development of neurotransmitter systems must be considered. Finally, the particular form of behaviour/characteristic outcome due to childhood circumstance may lie with biological, gene-based determinants, for example individual characteristics of monoamine oxidase (MAO) activity levels, thereby rendering simple predictive measures both redundant and misguiding.

In the age of increased international collaboration in medical research, the necessity of having at hand cross-culturally applicable instruments for the assessment of health-related quality of life (HRQL) in clinical trials has been voiced. Several important theoretical bases leading to cultural bias in HRQL measurement include differences in definitions of HRQL across national and cultural contexts, levels of observation relied upon to indicate HRQL states, and the significance or weight placed upon the various HRQL states or dimensions measured. Despite a growing literature on the development and evaluation of existing HRQL measures in other cultures, comprehensive sets of procedures or requirements for the international part of development and evaluation are lacking. This paper reviews major approaches to developing international HRQL measures, and discusses various methods and criteria that have been recommended for evaluating measurement equivalence in comparisons of research across national and cultural contexts. A summary of recent trends and advances in international HRQL assessment is presented.

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO (l1-penalized regression) and forward stagewise regression, and provides a fast imple- mentation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, we give an overview of Least Angle Regression and the current state of related research.

We investigate the relation between personality (Big Five) and positive and negative life events as pre- dictors of subjective well-being (SWB) in a sample of 766 young, middle-aged, and old adults. Analyses comprised data on personality, SWB, and reconstructed positive and negative life events. Results for the total sample indicate a strong relation between neuroticism and SWB, and an important influence of reconstructed life events on SWB with a stronger effect for negative as compared to positive events. Age differences in the prediction of SWB emerge for personality and life events: extraversion is only a predictor of SWB in young adults and the effect of neuroticism is more pronounced in old adults. More- over, the influence of negative life events on SWB is stronger in young and middle-aged adults as com- pared to old adults. These results emphasize the need to study dispositional and situational variables across the life span in order to better understand the underlying mechanisms of SWB.

The search for genes underlying complex traits has been difficult and often disappointing. The main reason for these difficulties is that several genes, each with rather small effect, might be interacting to produce the trait. Therefore, we must search the whole genome for a good chance to find these genes. Doing this with tens of thousands of SNP markers, however, greatly increases the overall probability of false-positive results, and current methods limiting such error probabilities to acceptable levels tend to reduce the power of detecting weak genes. Investigating large numbers of SNPs inevitably introduces errors (e.g., in genotyping), which will distort analysis results. Here we propose a simple strategy that circumvents many of these problems. We develop a set-association method to blend relevant sources of information such as allelic association and Hardy-Weinberg disequilibrium. Information is combined over multiple markers and genes in the genome, quality control is improved by trimming, and an appropriate testing strategy limits the overall false-positive rate. In contrast to other available methods, our method to detect association to sets of SNP markers in different genes in a real data application has shown remarkable success.

Human intelligence is a trait that is known to be significantly influenced by genetic factors, and recent linkage data provide positional evidence to suggest that a region on chromosome 6p, previously associated with schizophrenia, may be linked to variation in intelligence. The gene for dysbindin-1 (DTNBP1) is located at 6p and has also been implicated in schizophrenia, a neuropsychiatric disorder characterized by cognitive dysfunction. We report an association between DTNBP1 genotype and general cognitive ability (g) in two independent cohorts, including 213 patients with schizophrenia or schizo-affective disorder and 126 healthy volunteers. These data suggest that DTNBP1 genetic variation influences human intelligence.

OBJECTIVES: Current treatment guidelines recommend immediate modification of antiretroviral therapy in HIV-infected individuals with incomplete viral suppression. These recommendations have not been tested in observational studies or large randomized trials. We evaluated the consequences of delayed modification following virologic failure. DESIGN/METHODS: We used prospective data from two clinical cohorts to estimate the effect of time until regimen modification following first regimen failure on all-cause mortality. The impact of regimen type was also assessed. As the effect of delayed switching can be confounded if patients with a poor prognosis modify therapy earlier than those with a good prognosis, we used a statistical methodology - marginal structural models - to control for time-dependent confounding. RESULTS: A total of 982 patients contributed 3414 person-years of follow-up following first regimen failure. Delay until treatment modification was associated with an elevated hazard of all-cause mortality among patients failing a reverse transcriptase inhibitor-based regimen (hazard ratio per additional 3 months delay = 1.23, 95% confidence interval: 1.08, 1.40), but appeared to have a small protective effect among patients failing a protease inhibitor-based regimen (hazard ratio per additional 3 months delay = 0.93, 95% confidence interval: 0.87, 0.99). CONCLUSION: Delay in modification after failure of regimens that do not contain a protease inhibitor is associated with increased mortality. Protease inhibitor-based regimens are less dependent on early versus delayed switching strategies. Efforts should be made to minimize delay until treatment modification in resource-poor regions, where the majority of patients are starting reverse transcriptase inhibitor-based regimens and HIV RNA monitoring may not be available.

Cognitive diagnostic modeling has become an exciting new field of psychometric research. These models aim to diagnose examinees' mastery status of a group of discretely defined skills, or attributes, thereby providing them with detailed information regarding their specific strengths and weaknesses. Combining cognitive diagnosis with computer adaptive assessments has emerged as an important part of this new field. This article aims to provide practitioners and researchers with an introduction to and overview of recent developments in cognitive diagnostic computer adaptive assessments.

BACKGROUND: Aim of the COSMIN study (COnsensus-based Standards for the selection of health status Measurement INstruments) was to develop a consensus-based checklist to evaluate the methodological quality of studies on measurement properties. We present the COSMIN checklist and the agreement of the panel on the items of the checklist. METHODS: A four-round Delphi study was performed with international experts (psychologists, epidemiologists, statisticians and clinicians). Of the 91 invited experts, 57 agreed to participate (63%). Panel members were asked to rate their (dis)agreement with each proposal on a five-point scale. Consensus was considered to be reached when at least 67% of the panel members indicated 'agree' or 'strongly agree'. RESULTS: Consensus was reached on the inclusion of the following measurement properties: internal consistency, reliability, measurement error, content validity (including face validity), construct validity (including structural validity, hypotheses testing and cross-cultural validity), criterion validity, responsiveness, and interpretability. The latter was not considered a measurement property. The panel also reached consensus on how these properties should be assessed. CONCLUSIONS: The resulting COSMIN checklist could be useful when selecting a measurement instrument, peer-reviewing a manuscript, designing or reporting a study on measurement properties, or for educational purposes.

In a multiple testing problem where one is willing to tolerate a few false rejections, procedure controlling the familywise error rate (FWER) can potentially be improved in terms of its ability to detect false null hypotheses by generalizing it to control the $k$-FWER, the probability of falsely rejecting at least $k$ null hypotheses, for some fixed $k>1$. Simes' test for testing the intersection null hypothesis is generalized to control the $k$-FWER weakly, that is, under the intersection null hypothesis, and Hochberg's stepup procedure for simultaneous testing of the individual null hypotheses is generalized to control the $k$-FWER strongly, that is, under any configuration of the true and false null hypotheses. The proposed generalizations are developed utilizing joint null distributions of the $k$-dimensional subsets of the $p$-values, assumed to be identical. The generalized Simes' test is proved to control the $k$-FWER weakly under the multivariate totally positive of order two (MTP$_2$) condition [J. Multivariate Analysis 10 (1980) 467-498] of the joint null distribution of the $p$-values by generalizing the original Simes' inequality. It is more powerful to detect $k$ or more false null hypotheses than the original Simes' test when the $p$-values are independent. A stepdown procedure strongly controlling the $k$-FWER, a version of generalized Holm's procedure that is different from and more powerful than [Ann. Statist. 33 (2005) 1138-1154] with independent $p$-values, is derived before proposing the generalized Hochberg's procedure. The strong control of the $k$-FWER for the generalized Hochberg's procedure is established in situations where the generalized Simes' test is known to control its $k$-FWER weakly.

ABSTRACT: BACKGROUND: Although patients with Treatment Resistant Depression (TRD) often have impaired social functioning, few studies have investigated the effectiveness of psychosocial treatment for these patients. We examined whether adding group cognitive behavioral therapy (group-CBT) to medication would improve both the depressive symptoms and the social functioning of patient with mild TRD, and whether any improvements would be maintained over one year. METHODS: Forty-three patients with TRD were treated with 12 weekly sessions of group-CBT. Patients were assessed with the Global Assessment of Functioning scale (GAF), the 36-item Short-Form Health Survey (SF-36), the Hamilton Rating Scale for Depression (HRSD), the Dysfunctional Attitudes Scale (DAS), and the Automatic Thought Questionnaire-Revised (ATQ-R) at baseline, at the termination of treatment, and at the 12-month follow-up. RESULTS: Thirty-eight patients completed treatment; five dropped out. For the patients who completed treatment, post-treatment scores on the GAF and SF-36 were significantly higher than baseline scores. Scores on the HRSD, DAS, and ATQ-R were significantly lower after the treatment. Thus patients improved on all measurements of psychosocial functioning and mood symptoms. Twenty patients participated in the 12-month follow-up. Their improvements for psychosocial functioning, depressive symptoms, and dysfunctional cognitions were sustained at 12 months following the completion of group-CBT. CONCLUSIONS: These findings suggest a positive effect that the addition of cognitive behavioural group therapy to medication on depressive symptoms and social functioning of mildly depressed patients, showing treatment resistance.

BACKGROUND: Personality traits may form a part of the aetiology of opioid dependence. For instance, opioid dependence may result from self-medication in emotionally unstable individuals, or from experimenting with drugs in sensation seekers. The five factor model (FFM) has obtained a central position in contemporary personality trait theory. The five factors are: Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness. Few studies have examined whether there is a distinct personality pattern associated with opioid dependence. METHODS: We compared FFM personality traits in 65 opioid dependent persons (mean age 27 years, 34% females) in outpatient counselling after a minimum of 5 weeks in buprenorphine replacement therapy, with those in a non-clinical, age- and sex-matched sample selected from a national database. Personality traits were assessed by a Norwegian version of the Revised NEO Personality Inventory (NEO PI-R), a 240-item self-report questionnaire. Cohen's d effect sizes were calculated for the differences in personality trait scores. RESULTS: The opioid-dependent sample scored higher on Neuroticism, lower on Extraversion and lower on Conscientiousness (d = -1.7, 1.2 and 1.7, respectively) than the controls. Effects sizes were small for the difference between the groups in Openness to experience scores and Agreeableness scores. CONCLUSION: We found differences of medium and large effect sizes between the opioid dependent group and the matched comparison group, suggesting that the personality traits of people with opioid dependence are in fact different from those of non-clinical peers.

In item response models of the Rasch type (Fischer & Molenaar, 1995), item parameters are often estimated by the conditional maximum likelihood (CML) method. This paper addresses the loss of information in CML estimation by using the information concept of F-information (Liang, 1983). This concept makes it possible to specify the conditions for no loss of information and to define a quantification of information loss. For the dichotomous Rasch model, the derivations will be given in detail to show the use of the F-information concept for making efficiency comparisons for different estimation methods. It is shown that by using CML for item parameter estimation, some information is almost always lost. But compared to JML (joint maximum likelihood) as well as to MML (marginal maximum likelihood) the loss is very small. The reported efficiency of CML to JML and to MML in several comparisons is always larger than 93%, and in tests with a length of 20 items or more, larger than 99%.

BACKGROUND: There are few national or cross-cultural studies of the stigma associated with mental disorders. Australia and Japan have different systems of psychiatric health care, and distinct differences in cultural values, but enjoy similar standards of living. This study seeks to compare the nature and extent of stigma among the public in the two countries. METHODS: A household survey of the public was conducted in each country using similar methodologies. The Australian study comprised a national survey of 3998 adults aged over 18 years. The Japanese survey involved 2000 adults aged 20 to 69 from 25 regional sites distributed across the country. Interviewees reported their personal attitudes (personal stigma, social distance) and perceptions of the attitudes of others (perceived stigma, perceived discrimination) in the community with respect to four case vignettes. These vignettes described a person with: depression; depression with suicidal ideation; early schizophrenia; and chronic schizophrenia. RESULTS: Personal stigma and social distance were typically greater among the Japanese than the Australian public whereas the reverse was true with respect to the perception of the attitudes and discriminatory behaviour of others. In both countries, personal stigma was significantly greater than perceived stigma. The public in both countries showed evidence of greater social distance, greater personal stigma and greater perceived stigma for schizophrenia (particularly in its chronic form) than for depression. There was little evidence of a difference in stigma for depression with and without suicide for either country. However, social distance was greater for chronic compared to early schizophrenia for the Australian public. CONCLUSION: Stigmatising attitudes were common in both countries, but negative attitudes were greater among the Japanese than the Australian public. The results suggest that there is a need to implement national public awareness interventions tailored to the needs of each country. The current results provide a baseline for future tracking of national stigma levels in each country.

OBJECTIVE: Cancer-related fatigue (CRF) is a distressing, persistent, subjective sense of tiredness or exhaustion that occurs in 70-100% of cancer patients. The purpose of this review was to provide an overview of the quality of research performed on existing CRF self-report questionnaires and compare their reported psychometric properties and user-friendliness. METHODS: Database searches of CINAHL, Cochrane Library, EMBASE, MEDLINE, Scopus, PEDro, and PsycINFO were undertaken to find published scales. Standardized criteria were used to assess quality and user-friendliness. RESULTS: Thirty-five articles were included that described 18 questionnaires-seven one-dimensional questionnaires and 11 multidimensional questionnaires. The mean item count was 20.8 (range: 3-83). The mean overall score of the one-dimensional questionnaires was 10.4 of a maximum of 18 points (range: 7.6-14.3). The mean overall score of the multidimensional questionnaires was 9.4 of a maximum of 18 points (range: 4.3-14.4). CONCLUSION: Recommendations were made for the selection of a scale. We argue in favor of repeatedly reassessing psychometric properties of even established questionnaires to ensure they comply with evermore increasing stringent quality criteria.

BACKGROUND: The number of publications dealing with measurement of the quality of life and health in the area of drug dependence has increased in recent years. Its main application is as an indicator of the effectiveness of intervention in harm reduction, although there are also comparative and methodological studies. DATA SOURCES AND STUDY SELECTION: The literature was reviewed to identify studies on abuse or substance dependence and HRQoL. The bibliographic sources used for the review are PubMed, EMBASE, CINAHL and PsycInfo. Additional articles were identified from references to relevant articles. RESULTS: 111 articles were identified. The HRQoL of people who abuse or are dependent on substances is lower than the general population. The presence of physical and psychiatric comorbidity also affects patients dependent on opiates, and substitution programs improve HRQoL. CONCLUSION: The measurement of HRQoL in the area of drug dependence is a suitable complement for finding out the deterioration caused by substance use, abuse or dependence. It is also a useful indicator for evaluating therapeutic results in this population.

This article discusses the concept of an invariant item ordering (IIO) for polytom- ously scored items and proposes methods for investigating an IIO in real test data. Method manifest IIO is proposed for assessing whether item response functions intersect. Coefficient HT is defined for polytomously scored items. Given that an IIO holds, coefficient HT expresses the accuracy of the item ordering. Method manifest IIO and coefficient HT are used together to analyze a real data set. Topics for future research are discussed.

Effective visualization of biological data is often critical for subsequent analy- sis. The popular clustergram/dendrogram visualization rearranges rows and columns of a data matrix so as to highlight clusters of similar responses, but assumes each row or column belongs to only one cluster and cannot associate each row or column with multiple clusters. Such multi-way associations oc- cur frequently, e.g., when a gene plays multiple biological roles. We describe the 'factorgram' visualization, which rearranges the data into an expanded view, associating each row (or column) with multiple clusters of rows (or columns) and elucidating potentially new biological relationships. Factor- grams for mouse gene expression and yeast synthetic-lethal gene-interaction datasets detect a larger number of statistically-significant clusters than clus- tergrams, plus a larger number of clusters enriched for gene ontology annota- tions. Experimentally-verified associations previously identified by manual rearrangement of rows and columns not grouped together by clustergrams, are readily identified by the factorgram.

The TaqIA single nucleotide polymorphism (SNP, rs1800497), which is located in the gene that codes for the putative kinase ANKK1 (ANKK1) near the termination codon of the D2 dopamine receptor gene (DRD2; chromosome 11q22-q23), is the most studied genetic variation in a broad range of psychiatric disorders and personality traits. A large number of individual genetic association studies have found that the TaqIA SNP is linked to alcoholism and antisocial traits. In addition, it has also been related to other conditions such as schizophrenia, eating disorders, and some behavioral childhood disorders. The TaqIA A1 allele is mainly associated with addictions, antisocial disorders, eating disorders, and attention-deficit/hyperactivity disorders, while the A2 allele occurs more frequently in schizophrenic and obsessive-compulsive patients. Current data show that the TaqIA polymorphism may be a marker of both DRD2 and ANKK1 genetic variants. ANKK1 would belong to a family of kinases involved in signal transduction. This raises the question of whether signaling players intervene in the pathophysiology of psychiatric disorders. Basic research on the ANKK1 protein and its putative interaction with the D2 dopamine receptor could shed light on this issue.

Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but this paper argues that mixed-effects (multilevel) models provide a better alternative method. First, models are discussed in which the two random factors of participants and items are crossed, and not nested. Traditional ANOVAs are compared against these crossed mixed-effects models, for simulated and real data. Results indicate that the mixed-effects method has a lower risk of capitalization on chance (Type I error). Second, mixed-effects models of logistic regression (generalized linear mixed models, GLMM) are discussed and demonstrated with simulated binomial data. Mixed-effects models effectively solve the “language-as-fixed-effect-fallacy”, and have several other advantages. In conclusion, mixed-effects models provide a superior method for analyzing psycholinguistic data.

Genomic data are often characterized by a moderate to large number of categorical variables observed for relatively few subjects. Some of the variables may be missing or noninformative. An example of such data is loss of heterozygosity (LOH), a dichotomous variable, observed on a moderate number of genetic markers. We first consider a latent class model where, conditional on unobserved membership in one of k classes, the variables are independent with probabilities determined by a regression model of low dimension q. Using a family of penalties including the ridge and LASSO, we extend this model to address higher-dimensional problems. Finally, we present an orthogonal map that transforms marker space to a space of "features" for which the constrained model has better predictive power. We demonstrate these methods on LOH data collected at 19 markers from 93 brain tumor patients. For this data set, the existing unpenalized latent class methodology does not produce estimates. Additionally, we show that posterior classes obtained from this method are associated with survival for these patients.

The Temperament and Character Inventory (TCI) was developed to measure the following temperament dimensions: novelty seeking (NS), harm avoidance (HA), reward dependence (RD) and persistence (P). These four dimensions of temperament were originally proposed to be independent of one another. In this study the inter-relationships between the dimensions were studied with meta-analytic techniques. We also studied the effects of sociodemographic factors (location of the study, mean age and gender distribution) on correlations between temperament dimensions. We searched studies on healthy (non-clinical) populations that used the TCI (version 9), and that had a required sample size of at least 100. The search resulted in 16 articles. The resulted pooled correlation coefficient was medium level between NS and HA (-0.27). Correlations were small for HA-P (-0.20), NS-P (-0.14), NS-RD (0.10), RD-P (0.05) and HA-RD (0.04). In meta-regression, the correlation NS-P was significantly affected by the location of the study (Asian/other) and by the gender distribution of the sample. In the HA-P correlation, the mean age of the sample affected the correlation. In conclusion, we found a medium level negative correlation between NS and HA; other correlations between the dimensions were small. These findings mainly support Cloninger's theory of independent dimensions.

There is increasing interest in measuring health related quality of life in cancer clinical trials. Most quality of life data are measured repeatedly over a fixed time schedule to capture changes and to reflect relative advantages of study treatments. A multivariate repeated measures model is usually used to analyse this type of data. However, one of the difficulties of this analysis is that quality of life may be affected by the occurrence of some critical events experienced by patients. We may separate a patient's lifetime during study into different 'health states'. The duration of these health states may vary among patients, and may relate to the efficacy of the study treatment. In some cases quality of life data may be missing due to one of the many different types of missing data mechanisms specific for a health state. It is reasonable to assume that the missing data mechanism for a treatment arm is homogeneous within a defined health state, and to control for the potential confounding effect to appropriately assess the impact of treatment on the quality of life. In this paper, we propose a growth curve model conditional on a time-dependent variable of defined health states in order to assess the overall treatment effect while taking into account occurrences of missing data and measurements from irregular visits. A specific contrast can be drawn within the overall model for testing a specific hypothesis without relying on the analysis of subgroups of patients based on a smaller number of repeated measurements. Quality of life data from a recently completed small-cell lung cancer randomized trial are used to illustrate this method.

Two types of global testing procedures for item fit to the Rasch model were evaluated using simulation studies. The first type incorporates three tests based on first-order statistics: van den Wollenberg's Q(1) test, Glas's R(1) test, and Andersen's LR test. The second type incorporates three tests based on second-order statistics: van den Wollenberg's Q(2) test, Glas's R(2) test, and a non-parametric test proposed by Ponocny. The Type I error rates and the power against the violation of parallel item response curves, unidimensionality and local independence were analysed in relation to sample size and test length. In general, the outcomes indicate a satisfactory performance of all tests, except the Q(2) test which exhibits an inflated Type I error rate. Further, it was found that both types of tests have power against all three types of model violation. A possible explanation is the interdependencies among the assumptions underlying the model.

OBJECTIVES: The aim of this study was to shorten the Health-Related Quality of Life (HRQL) DISABKIDS Chronic Generic Measure (DCGM) for children and adolescents and to test its reliability, construct, and external validity. STUDY DESIGN: 1153 children and adolescents (8-16 years) with chronic health conditions (asthma, arthritis, epilepsy, cerebral palsy, diabetes, atopic dermatitis, cystic fibrosis) and their family were recruited from different paediatric clinical settings in seven European countries. A two-time assessment comprised reports on sociodemographics, health status and HRQL of children/adolescents. RESULTS: The 37-item DCGM describes six dimensions (Independence, Physical Limitation, Emotion, Social Inclusion, Social Exclusion and Treatment) confirmed by Confirmatory Factor Analysis, multi-item scaling and item-goodness of fit to Rasch model. Internal consistency (Cronbach's alpha: 0.70-0.87) and test-retest reliability (ICC: 0.71-0.83) were satisfactory. Correlations between DCGM-37 and other HRQL instruments were the highest between dimensions evaluating similar concepts. Regarding discriminant validity of the DCGM-37, girls and older adolescents reported lower emotional we ll-being. Children belonging to families with low level of affluence and those with severe health conditions were found to have worse HRQL in all domains. CONCLUSION: Reliability, construct validity as well as convergent and discriminant validity of the DCGM-37 were shown.

BACKGROUND: Residual depressive symptoms are generally documented as a risk factor for recurrence. In the absence of a specific instrument for the assessment of residual symptoms, a new 25-item Depression Residual Symptom Scale (DRSS) was elaborated and tested for recurrence prediction over a 1-year follow-up. SAMPLING AND METHODS: Fifty-nine patients in remission after a major depressive episode (MDE) were recruited in two centres. They were assessed with the DRSS and the Montgomery-Asberg Depression Rating Scale (MADRS) at inclusion and followed for 1 year according to a seminaturalistic design. The DRSS included specific depressive symptoms and subjective symptoms of vulnerability, lack of return to usual self and premorbid level of functioning. RESULTS: Severity of residual symptoms was not significantly associated with increased risk of recurrence. However, DRSS score was significantly higher among patients with three or more episodes than one to two episodes. Number of previous episodes and treatment interruption were not identified as significant predictors of recurrence. CONCLUSION: The proposed instrument is not predictive of depressive recurrence, but is sensitive to increased perception of vulnerability associated with consecutive episodes. Limitations include small sample size, seminaturalistic design (no standardisation of treatment) and content of the instrument.

BACKGROUND: The widespread international use of the 26-item WHO Quality of Life Instrument (WHOQOL-Bref) necessitates the assessment of its factor structure across cultures. For, alternative factor models may provide a better explanation of the data than the WHO 4- and 6-domain models. The objectives of the study were: to assess the factor structure of the WHOQOL-Bref in a Sudanese general population sample; and use confirmatory factor analysis (CFA) and path analysis (PA) to see how well the model thus generated fits into the WHOQOL-Bref data of Sudanese psychiatric patients and their family caregivers. METHOD: In exploratory factor analysis (FA) with all items, data from 623 general population subjects were used to generate a 5-domain model. In CFA and PA, the model was tested on the data of 300 psychiatric outpatients and their caregivers, using four goodness of fit (GOF) criteria in Analysis of Moment Structures (AMOS). In the path relationships for our model, the dependent variable was the item on overall QOL (OQOL). For the WHO 6-domain model, the general facet on health and QOL was the dependent variable. RESULTS: Two of the five factors ("personal relations" and "environment") from our FA were similar to the WHO's. In CFA, the four GOF criteria were met by our 5-domain model and WHO's 4-domain model on the psychiatric data. In PA, these two models met the GOF criteria on the general population data. The direct predictors of OQOL were our factors: "life satisfaction" and "sense of enjoyment". For the general facet, predictors were WHO domains: "environment", "physical health" and "independence'. CONCLUSION: The findings support the credentials of WHO's 4-domain model as a universal QOL construct; and the impression that analysis of WHOQOL-Bref could benefit from including all the items in FA and using OQOL as a dependent variable. The clinical significance is that by more of such studies, a combination of domains from the WHO models and the local models would be generated and used to develop rigorous definitions of QOL, from which primary targets for subjective QOL interventions could be delineated that would have cross-cultural relevance.

Using a self-administered questionnaire, 149 respondents rated service elements associated with a recently visited store or restaurant on scales that differed only in the number of response categories (ranging from 2 to 11) and on a 101-point scale presented in a different format. On several indices of reliability, validity, and discriminating power, the two-point, three-point, and four-point scales performed relatively poorly, and indices were significantly higher for scales with more response categories, up to about 7. Internal consistency did not differ significantly between scales, but test-retest reliability tended to decrease for scales with more than 10 response categories. Respondent preferences were highest for the 10-point scale, closely followed by the seven-point and nine-point scales. Implications for research and practice are discussed.

In this article, a distinction is made between absolute and relative measurement. Absolute measurement refers to the measurement of traits on a group-invariant scale, and relative measurement refers to the within-group measurement of traits, where the scale of measurement is expressed in terms of the within-group position on a trait. Relative measurement occurs, for example, if an item induces a within-group comparison in respondents. These distinctions are discussed within the framework of measurement invariance, differentiating between absolute and relative forms of measurement invariance and bias. It is shown that items for relative measurement will produce bias as classically defined if the mean and/or variance of the trait distribution differ between groups. This form of bias, however, does not result from multidimensionality but from the fact that measurement is on a relative scale. A logistic regression procedure for the detection of relative measurement invariance and bias is proposed, as well as a model that allows for the incorporation of items for relative measurement in test analysis. Implications of the distinction between absolute and relative measurement are discussed and prove to be especially relevant for the domain of personality research.

The relationship between dimensions of personality characteristics and the perceived rearing attitude of parents in the Japanese population were investigated. The scores on a measure of perceived parental attitude of 153 normal female students, measured on the Parker Parental Bonding Instrument, were correlated with personality features from the Japanese version of the Cloninger Temperament and Character Inventory. Self-directedness, especially the subclasses of Responsibility vs Blaming and Congruent Second Nature vs Incongruent Habits, was significantly related to high scores on Maternal Care and low scores on Maternal Overprotection. The subscale of Self-acceptance vs Self-striving correlated only with low scores on Maternal Overprotection. Paternal Care was only related to the total scale scores on Self-directedness. Results suggest that some personality traits may be related to the perceived attitudes of parents, especially of the mother, during childhood.

OBJECTIVE: As the nation debates issues of national health care reform, psychiatrists seek equal status with other medical colleagues. To defend psychiatry in the national arena, the accuracy of psychiatric diagnoses must be measured. Indexes of accuracy such as sensitivity and specificity provide valuable information, yet they are rarely computed because there is no "gold standard" with which to compare them. The goal of this article is to show how this problem can be overcome and to encourage nosologists to use accuracy statistics in assessing the adequacy of psychiatric diagnoses. METHOD: The authors reviewed the literature on medical decision making to find methodological approaches to assessing diagnostic accuracy in the absence of gold standards. RESULTS: A lack of such standards is not unique to psychiatry and has been addressed with a variety of novel analytic procedures. Although these methods differ in many respects, each recognizes that the conventional 2 x 2 table of interrater agreement does not provide enough data for estimating diagnostic accuracy. After defining the data needed, each method provides a mathematical model that estimates accuracy statistics and the prevalence of a disorder. Most of these methods are variants of latent class analysis. The authors reanalyzed data from one of the reviewed papers to show that similar inferences about accuracy of diagnoses could be drawn from a conventional latent class analysis. CONCLUSIONS: There are potential pitfalls in using latent structure methods, but their cautious use would provide valuable information for psychiatric nosology. These methods supplement, but do not replace, data about outcome, family history, laboratory studies, and other validating criteria in making accurate diagnoses.

BACKGROUND: In this paper we compare the results in an analysis of determinants of caregivers' health derived from two approaches, a structural equation model and a log-linear model, using the same data set. METHODS: The data were collected from a cross-sectional population-based sample of 468 families in Ontario, Canada who had a child with cerebral palsy (CP). The self-completed questionnaires and the home-based interviews used in this study included scales reflecting socio-economic status, child and caregiver characteristics, and the physical and psychological well-being of the caregivers. Both analytic models were used to evaluate the relationships between child behaviour, caregiving demands, coping factors, and the well-being of primary caregivers of children with CP. RESULTS: The results were compared, together with an assessment of the positive and negative aspects of each approach, including their practical and conceptual implications. CONCLUSION: No important differences were found in the substantive conclusions of the two analyses. The broad confirmation of the Structural Equation Modeling (SEM) results by the Log-linear Modeling (LLM) provided some reassurance that the SEM had been adequately specified, and that it broadly fitted the data.

ABSTRACT: Parents of children with a chronic condition such as juvenile arthritis must cope with greater demands than those living with a healthy child. They must adopt different behaviours in order to lessen the impact on the family structure. Parental coping refers to the parent's specific cognitive and behavioural efforts to reduce or manage a demand on the family system. The aims of this study were: to describe coping in a cohort of parents of children with JIA; to determine whether quality of life is associated with parental coping; to explore whether socio-demographic factors such as child's age, family socioeconomic status and family structure are associated with parental coping. One hundred eighty-two parents caring for a child with JIA completed a postal survey at three times over a one-year period, which included the Juvenile Arthritis Quality of Life Questionnaire (JAQQ), the Coping Health Inventory for Parents (CHIP) and questionnaires describing socio-demographic characteristics. Linear mixed models were employed to analyse the association between the child's quality of life and parental coping. Mean total QoL scores (JAQQ) showed that children experienced difficulty in completing specified activities at most just below 25% of the time and results fall off slightly following the 6 month time point. Mean parental coping scores for the CHIP subscales at baseline were 38.4 +/- 9.0, 33.4 +/- 11.6, 16.5 +/- 6.1, for Maintaining Family Integration (maximum score 57), Maintaining Social Support (maximum score 54) and Understanding the Medical Situation (maximum score 24), respectively. Understanding the Medical Situation was deemed most useful. The child's QoL was associated with parental coping. Parents of children with greater psychosocial dysfunction used more coping behaviours related to Understanding the Medical Situation (beta coefficient, 0.73; 95% CI, 0.01, 1.45). These findings underscore the importance of helping parents of children with JIA better understand their child's medical situation.

ABSTRACT: BACKGROUND: The current study explored the prevalence of depressed mood among Chinese undergraduate students and examined the coping patterns and degree of flexibility of flexibility of such patterns associated with such mood. METHODS: A set of questionnaire assessing coping patterns, coping flexibility, and depressive symptoms were administered to 428 students (234 men and 194 women). RESULTS: A total of 266 participants both completed the entire set of questionnaires and reported a frequency of two or more stressful life events (the criterion needed to calculate variance in perceived controllability). Findings showed that higher levels of depressive symptoms were significantly associated with higher levels of both event frequency (r = .368, p < .001) and event impact (r = .245, p < .001) and lower levels of perceived controllability (r = -.261, p < .001), coping effectiveness (r = -.375, p < .001), and ratio of strategy to situation fit (r = -.108, p < .05). Depressive symptoms were not significantly associated with cognitive flexibility (variance of perceived controllability; r = .031, p = .527), Gender was not a significant moderator of any of the reported associations. CONCLUSIONS: Findings indicate that Chinese university students with depressive symptoms reported experiencing a greater number of negative events than did non-depressed university students. In addition, undergraduates with depressive symptoms were more likely than other undergraduates to utilize maladaptive coping methods. Such findings highlight the potential importance of interventions aimed at helping undergraduate students with a lower coping flexibility develop skills to cope with stressful life events.

The inherent imprecision of behavioral phenotyping is the single most important factor contributing to the failure to discover the biological factors that are involved in psychiatric and neurodevelopmental disorders (e.g., Bearden & Freimer, 2006). In this review article we argue that in addition to an appreciation of the inherent complexity at the biological level, a rather urgent task facing behavioral scientists involves a reconsideration of the role that clinical syndromes play in psychological theorizing, as well as in research into the biological basis of cognition and personality. Syndrome heterogeneity, cross-syndrome similarities and syndrome comorbidities question the relevance of syndromes to biological research. It is suggested that the search for brain endophenotypes, intermediate between genes and behavior, should be based on cross-syndrome, trait classification. Cohort selection should rest on behavioral homogeneity, enabling, when necessary, syndrome heterogeneity.

Microarrays have become an indispensable tool in biomedical research. This powerful technology not only makes it possible to quantify a large number of nucleic acid molecules simultaneously, but also produces data with many sources of noise. A number of preprocessing steps are therefore necessary to convert the raw data, usually in the form of hybridisation images, to measures of biological meaning that can be used in further statistical analysis. Preprocessing of oligonucleotide arrays includes image processing, background adjustment, data normalisation/transformation and sometimes summarisation when multiple probes are used to target one genomic unit. In this article, we review the issues encountered in each preprocessing step and introduce the statistical models and methods in preprocessing.

Systematic reviews systematically evaluate and summarize current knowledge and have many advantages over narrative reviews. Meta-analyses provide a more reliable and enhanced precision of effect estimate than do individual studies. Systematic reviews are invaluable for defining the methods used in subsequent studies, but, as retrospective research projects, they are subject to bias. Rigorous research methods are essential, and the quality depends on the extent to which scientific review methods are used. Systematic reviews can be misleading, unhelpful, or even harmful when data are inappropriately handled; meta-analyses can be misused when the difference between a patient seen in the clinic and those included in the meta-analysis is not considered. Furthermore, systematic reviews cannot answer all clinically relevant questions, and their conclusions may be difficult to incorporate into practice. They should be reviewed on an ongoing basis. As clinicians, we need proper methodological training to perform good systematic reviews and must ask the appropriate questions before we can properly interpret such a review and apply its conclusions to our patients. This paper aims to assist in the reading of a systematic review.

From the single-author composition of a Bachelor thesis to the creation of a book by a team there are many occasions, where version management of a document may be helpful. With the aim of overcoming the shortcomings of CVS (Concurrent Version System) the Subversion version control system was implemented. In this article I will describe the Subversion setup on Windows and Linux systems, the elementary steps of document management and various LATEX packages working hand in hand with Subversion.

The concept of covariate adjustment is well established in therapeutic and etiologic studies. However, it has received little attention in the growing area of medical research devoted to the development of markers for disease diagnosis, screening, or prognosis, where classification accuracy, rather than association, is of primary interest. In this paper, the authors demonstrate the need for covariate adjustment in studies of classification accuracy, discuss methods for adjusting for covariates, and distinguish covariate adjustment from several other related, but fundamentally different, uses for covariates. They draw analogies and contrasts throughout with studies of association.

The mathematical model of the widely-used sparse covariance selection problem (SCSP) is an NP-hard combinatorial problem, whereas it can be well approximately by a convex relaxation problem whose maximum likelihood estimation is penalized by the L1 norm. This convex relaxation problem, however, is still numerically challenging, especially for large-scale cases. Recently, some efficient first-order methods inspired by Nesterov's work have been proposed to solve the convex relaxation problem of SCSP. This paper is to apply the well-known alternating direction method (ADM), which is also a first-order method, to solve the convex relaxation of SCSP. Due to the full exploitation to the separable structure of a simple reformulation of the convex relaxation problem, the ADM approach is very efficient for solving large-scale SCSP. Our preliminary numerical results show that the ADM approach substantially outperforms existing first-order methods for SCSP.

Structural equation modeling (SEM) is a multivariate technique suited for testing proposed relations between variables. In this article, the authors discuss the potential for SEM as a tool to advance health communication research both statistically and conceptually. Specifically, the authors discuss the advantages that latent variable modeling in SEM affords researchers by extracting measurement error. In addition, they argue that SEM is useful in understanding communication as a complex set of relations between variables. Moreover, the authors articulate the possibility for examining communication as an agent, mediator, and an outcome. Finally, they review the application of SEM to recursive models, interactions, and confirmatory factor analysis.

In the last years of his life, Leo Breiman promoted random forests for use in classification. He suggested using averaging as a means of obtaining good discrimination rules. The base classifiers used for averaging are simple and randomized, often based on random samples from the data. He left a few questions unanswered regarding the consistency of such rules. In this paper, we give a number of theorems that establish the universal consistency of averaging rules. We also show that some popular classifiers, including one suggested by Breiman, are not universally consistent.

The purpose of this literature review is to examine what makes reading for understanding especially challenging for children on the autism spectrum, most of whom are skilled at decoding and less skilled at comprehension. This paper first summarizes the research on reading comprehension with a focus on the cognitive skills and processes that are involved in gaining meaning from text and then reviews studies of reading comprehension deficits in children on the spectrum. The paper concludes with a review of reading comprehension interventions for children on the spectrum. These children can especially benefit from interventions addressing particular cognitive processes, such as locating antecedent events, generating and answering questions, locating referents, and rereading to repair understanding.

In this study the Mantel-Haenszel procedure, widely used in studies for identifying differential item functioning, is proposed as an alternative to the delta-plot method and applied in a test-equating context for flagging common items that behave differentially across cohorts of examinees. The Mantel-Haenszel procedure has the advantage of conditioning on ability when making comparisons of performance of two examinee groups on an item. There are schemes for interpreting the effect size of differential performance, which can inform the decision as to whether to retain those items in the common-item pool, or to discard them. Data from a statewide assessment are analyzed to illustrate the use of this procedure. Advantages of this methodology are discussed and limitations regarding test design that may make its application difficult are described.

We hypothesize that when two pathological conditions or personality traits share the same critical period for gene-environment interaction, we should expect further similarities, particularly from clinical and pathophysiological perspectives. They should therefore be considered as two facets of the same disease. To test this hypothesis we compiled data included in the Primal Health Research Database. This database (www.primalhealthresearch.com) is specialised in studies exploring correlations between what happens during the 'primal period' (fetal life, perinatal period and year following birth) and what happens later on in life in terms of health and personality traits. After mentioning the links between autism and anorexia nervosa, we explore more in depth the links between attention deficit hyperactivity disorder (ADHD) and obesity. We suggest from such examples that the nature of an environmental factor is often less important than the timing of the interaction. We conclude that the concept of gene expression, combined with Primal Health Research, might lead to reconsider conventional nosological classifications. Some previously well-defined pathological entities should be included into the framework of multifaceted diseases. On the other hand some existing pathological entities should be dismantled.

We generated a high-resolution whole-genome sequence and individually deleted 5100 genes in Sigma1278b, a Saccharomyces cerevisiae strain closely related to reference strain S288c. Similar to the variation between human individuals, Sigma1278b and S288c average 3.2 single-nucleotide polymorphisms per kilobase. A genome-wide comparison of deletion mutant phenotypes identified a subset of genes that were conditionally essential by strain, including 44 essential genes unique to Sigma1278b and 13 unique to S288c. Genetic analysis indicates the conditional phenotype was most often governed by complex genetic interactions, depending on multiple background-specific modifiers. Our comprehensive analysis suggests that the presence of a complex set of modifiers will often underlie the phenotypic differences between individuals.

BACKGROUND AND OBJECTIVE: Measuring physical functioning (PF) within and across postacute settings is critical for monitoring outcomes of rehabilitation; however, most current instruments lack sufficient breadth and feasibility for widespread use. Computer adaptive testing (CAT), in which item selection is tailored to the individual patient, holds promise for reducing response burden, yet maintaining measurement precision. We calibrated a PF item bank via item response theory (IRT), administered items with a post hoc CAT design, and determined whether CAT would improve accuracy and precision of score estimates over random item selection. METHODS: 1,041 adults were interviewed during postacute care rehabilitation episodes in either hospital or community settings. Responses for 124 PF items were calibrated using IRT methods to create a PF item bank. We examined the accuracy and precision of CAT-based scores compared to a random selection of items. RESULTS: CAT-based scores had higher correlations with the IRT-criterion scores, especially with short tests, and resulted in narrower confidence intervals than scores based on a random selection of items; gains, as expected, were especially large for low and high performing adults. CONCLUSION: The CAT design may have important precision and efficiency advantages for point-of-care functional assessment in rehabilitation practice settings.

BACKGROUND: Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. RESULTS: In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. CONCLUSION: This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

This paper aims at proposing the joint use of Canonical Correlation Analysis and Procrustes Rotations (RCA), when we deal with a text and its translation into another language. The basic idea is representing words in the two different natural languages on a common reference space. The main characteristic of this space is to be lan- guage independent, although Procrustes Rotation is performed transforming the lexical table derived from trans- lation by minimizing its distance from the lexical table belonging to the original corpus, while the subsequent Canonical Correlation Analysis treats symmetrically the two word sets. The most interesting RCA feature is building a unique reference space for representing the correlation structure in the data, inducing the two systems of canonical factors to lie on the same space. These graphical representations enables us to read distances be- tween corresponding points in terms of different way of translating the same word in relation with the general context defined by the canonical variates. Trying to understand the distances between matched points could rep- resent an useful tool for enriching lexical resources in a translation procedure. In this paper we propose the com- parison of the most frequent content bearing words in the two languages, analyzing one year (2003) of Le Monde Diplomatique and its Italian edition.

OBJECTIVE: To develop a classification system for blepharitis and dry eye based on a classification-tree model of a large group of subjects who were given a variety of objective physiologic tests. METHODS: We evaluated 513 subjects, some healthy and some with blepharitis and dry eye,with tests for tear volume, tear flow, and tear turnover and the Schirmer test for dry eye. Meibomian gland function was evaluated by meibomian gland lipid expression for lipid volume and lipid viscosity, evaporation, and eyelid transillumination for meibomian gland drop out. We subjected these data to cluster analysis and formulated a classification tree. MAIN OUTCOME MEASURE: The outcome measure of this study was the statistically valid groups of subjects with and without ocular surface symptoms identified by their physiologic characteristics. RESULTS: Cluster analysis most successfully grouped subjects by initially dividing them into 2 groups based on the presence or absence of gland drop out and then by lipid viscosity and volume, Schirmer test results, and evaporation. The analysis created 9 categories. This division created an objective classification system that was found to have clinical relevance. Normal subjects were distributed across several groups. CONCLUSIONS: Using a classification tree, blepharitis and dry eye can be classified with objective physiologic tests into clinically relevant groups that have common characteristics. The analysis establishes the central role of meibomian gland dysfunction in blepharitis and demonstrates the diverse characteristics of the normal population.

Experimental psychologists routinely simplify the structure of their data by computing means for each experimental condition so that the basic assumptions of regression/ANOVA are satisfied. Typically, these means represent the performance (e.g. reaction time or RT) of a participant over several items that share some target characteristic (e.g. Mean RT for high‐frequency words). Regrettably, analyses based on such aggregated data are biased toward rejection of the null hypothesis, inflating Type‐I error beyond the nominal level. A preferable strategy for analyzing such data is random coefficient analysis (RCA), which can be performed using a simple method proposed by Lorch & Myers (1990). An easy to use SPSS implementation of this method is presented using a concrete example. In addition, a technique for evaluating the magnitude of potential aggregation bias in a dataset is demonstrated. Finally, suggestions are offered concerning the reporting of RCA results in empirical articles.

We consider estimating an unknown signal, which is both blocky and sparse, corrupted by additive noise. We study three interrelated least squares procedures and their asymptotic properties. The first procedure is the fused lasso, put forward by Friedman et al. (2007), which we modify into a different estimator, called the fused adaptive lasso, with better properties. The other two estimators we discuss solve least squares problems on sieves, one constraining the maximal l1 norm and the maximal total variation seminorm, the other restricting the number of blocks and the number of of nonzero coordinates of the signal. We derive conditions for the recovery of the true block partition and the true sparsity patterns by the fused lasso and the fused adaptive lasso, and convergence rates for the sieve estimators, explicitly in terms of the constraining parameters.

OBJECTIVES: Although sexual dysfunction has been reported in patients with hepatitis C virus (HCV) infection, little is known about this association. The aims of this study were to determine the prevalence of sexual dysfunction among men with chronic HCV infection and to evaluate the impact of sexual dysfunction on health-related quality of life (HRQOL). METHODS: We prospectively enrolled 112 HCV positive men and 239 HCV negative controls, and all patients completed validated questionnaires to assess sexual function (Brief Male Sexual Function Inventory [BMSFI]), depression (Beck Depression Inventory), and HRQOL (Medical Outcomes Study Short Form-36). The BMSFI assessed sexual drive, erection, ejaculation, sexual problem assessment, and overall sexual satisfaction. RESULTS: HCV positive men had significantly more sexual dysfunction than control subjects across all five domains of the BMFSI. In addition, HCV-infected men were significantly more likely than controls to not be sexually satisfied (53.6% vs 28.9%, p<0.001) and this remained statistically significant after adjusting for age, race, and other potential confounding variables (OR=3.36; 95% CI, 1.59-7.13). In the 241 individuals without depression, HCV positive men were significantly more likely to not be sexually satisfied as compared with control subjects (47.5% vs 11.0%, p<0.001). HCV-infected men who were not sexually satisfied scored significantly worse in six of eight domains of HRQOL as compared with HCV-infected men who were sexually satisfied. CONCLUSIONS: Sexual dysfunction is highly prevalent in men with chronic HCV infection, is independent of depression, and is associated with a marked reduction in HRQOL.

In this report, we present the first regional quantitative analysis of age-related differences in the heritability of cortical thickness using anatomic MRI with a large pediatric sample of twins, twin siblings, and singletons (n = 600, mean age 11.1 years, range 5-19). Regions of primary sensory and motor cortex, which develop earlier, both phylogenetically and ontologically, show relatively greater genetic effects earlier in childhood. Later developing regions within the dorsal prefrontal cortex and temporal lobes conversely show increasingly prominent genetic effects with maturation. The observation that regions associated with complex cognitive processes such as language, tool use, and executive function are more heritable in adolescents than children is consistent with previous studies showing that IQ becomes increasingly heritable with maturity(Plomin et al. 1997: Psychol Sci 8:442-447). These results suggest that both the specific cortical region and the age of the population should be taken into account when using cortical thickness as an intermediate phenotype to link genes, environment, and behavior.

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases, and array-based CGH (aCGH) is currently the main technology to lo- cate CNVs. Although many methods have been developed to analyze aCGH from a single array/subject, disease-critical genes are more likely to be found in regions that are common or recurrent among subjects. Unfortunately, finding recurrent CNV regions remains a challenge. We review existing methods for the identifi- cation of recurrent CNV regions. The working definition of “common” or “recurrent” region differs between methods, leading to approaches that use different types of input (discretized output from a previous CGH segmentation analysis or intensity ratios), or that incorporate to varied degrees biological considerations (which play a role in the identification of “interesting” regions and in the details of null models used to assess statistical significance). Very few approaches use and/or return probabilities, and code is not easily available for several methods. We suggest that finding recurrent CNVs could benefit from reframing the problem in a biclustering context. We also emphasize that, when analyzing data from complex diseases with significant among-subject heterogeneity, methods should be able to identify CNVs that affect only a subset of subjects. We make some recommendations about choice among existing methods, and we suggest further methodological research.

A computer-intensive process was performed to simulate 12,600 data sets each with n = 5,000 individuals from distinct pedigree structures to assess the effect of pedigree information on the sampling variance of heritability (h2) estimates. Pedigree structures were determined by varying the proportion of foundation animals (PF), percentage replacement rates for males (RM) and females (RF), and ratio of females to male (F2M). A 2(3) factorial design was modeled; levels of RM and RF were 10 and 20%, and levels of F2M were 10 and 20. For each of the eight cells, 60 foundation animals were simulated, each with 10 replicates. The required mating seasons (MS) to obtain the number of individuals was simulated based on PF and F2M. A REML algorithm was used to estimate h2 and its associated SE. The effect of all factors was analyzed in a regression model with linear and quadratic components for PF. An alternative model with MS replacing PF was also investigated. There was a non-monotonic association (P < .01) between PF and h2 SE. The minimum h2 SE occurred when PF ranged from 20 to 40%. Here, the proportion of first-generation progeny was near its maximum with rapid increases in the proportion of subsequent descendants. Among the class effects, F2M yielded the highest mean square (P < .001). When considering more than one MS, h2 SE was positively associated (P < .01) with RF and F2M and negatively associated with RM. Results suggest that h2 is most accurately estimated when there is performance information on many animals closely related to foundation animals.

MOTIVATION: Microarray experiments result in large-scale data sets that require extensive mining and refining to extract useful information. We demonstrate the usefulness of (non-metric) multidimensional scaling (MDS) method in analyzing a large number of genes. Applying MDS to the microarray data is certainly not new, but the existing works are all on small numbers (< 100) of points to be analyzed. We have been developing an efficient novel algorithm for non-metric MDS (nMDS) analysis for very large data sets as a maximally unsupervised data mining device. We wish to demonstrate its usefulness in the context of bioinformatics (unraveling relational patterns among genes from time series data in this paper). RESULTS: The Pearson correlation coefficient with its sign flipped is used to measure the dissimilarity of the gene activities in transcriptional response of cell-cycle-synchronized human fibroblasts to serum. These dissimilarity data have been analyzed with our nMDS algorithm to produce an almost circular relational pattern of the genes. The obtained pattern expresses a temporal order in the data in this example; the temporal expression pattern of the genes rotates along this circular arrangement and is related to the cell cycle. For the data we analyze in this paper we observe the following. If an appropriate preparation procedure is applied to the original data set, linear methods such as the principal component analysis (PCA) could achieve reasonable results, but without data preprocessing linear methods such as PCA cannot achieve a useful picture. Furthermore, even with an appropriate data preprocessing, the outcomes of linear procedures are not as clear-cut as those by nMDS without preprocessing.

This review examines recent advances in sample size planning, not only from the perspective of an individual researcher, but also with regard to the goal of developing cumulative knowledge. Psychologists have traditionally thought of sample size planning in terms of power analysis. Although we review recent advances in power analysis, our main focus is the desirability of achieving accurate parameter estimates, either instead of or in addition to obtaining sufficient power. Accuracy in parameter estimation (AIPE) has taken on increasing importance in light of recent emphasis on effect size estimation and formation of confidence intervals. The review provides an overview of the logic behind sample size planning for AIPE and summarizes recent advances in implementing this approach in designs commonly used in psychological research.

Scales that measure the Big Five personality factors are often substantially intercorrelated. These correlations are sometimes interpreted as implying the existence of two higher order factors of personality. The authors show that correlations between measures of broad personality factors do not necessarily imply the existence of higher order factors and might instead be due to variables that represent same-signed blends of orthogonal factors. Therefore, the hypotheses of higher order factors and blended variables can only be tested with data on lower level personality variables that define the personality factors. The authors compared the higher order factor model and the blended variable model in three participant samples using the Big Five Aspect Scales, and found better fit for the latter model. In other analyses using the HEXACO Personality Inventory, they identified mutually uncorrelated markers of six personality factors. The authors conclude that correlations between personality factor scales can be explained without postulating any higher order dimensions of personality.

BACKGROUND: The majority of affective psychopathology is rooted early in life and first emerges during childhood and adolescence. However, little is known about how genetic vulnerability affects brain structure and function in childhood since the vast majority of studies published so far have been conducted on adult participants. The present investigation examined for the first time the effects of catechol-O-methyltransferase (COMT) valine (val) 158 methionine (met) (val158met) polymorphism, which has been shown to moderate predisposition to negative mood and affective disorders, on brain structure and function in children. METHODS: Voxel-based morphometry and functional magnetic resonance imaging were used to measure gray matter volume and emotional reactivity in 50 children aged between 10 and 12 years. We tested the hypothesis that met158 allele affects structural brain development and confers heightened reactivity within the affective frontolimbic circuit in children. RESULTS: The met158 allele was positively associated with gray matter volume in the left hippocampal head where genotype accounted for 59% of interindividual variance. In addition, the met158 allele was positively associated with neuronal responses to fearful relative to neutral facial expressions in the right parahippocampal gyrus where genotype accounted for 14% of the interindividual variance. CONCLUSIONS: These results indicate that the met158 allele is associated with increased gray matter volume and heightened reactivity during emotional processing within the limbic system in children as young as 10 to 12 years of age. These findings are consistent with the notion that genetic factors affect brain function to moderate vulnerability to affective psychopathology from childhood.

BACKGROUND: Although parent-proxy reports of health-related quality of life (HRQOL) are only moderately correlated with child reported HRQOL, it remains unknown why these scores differ. The aim of this study was to use a qualitative methodology to examine why parents and children report different levels of HRQOL. METHOD: The sample consisted of 15 parent-child pairs. A think-aloud technique was used where parents and children were given a generic HRQOL instrument (KIDSCREEN) and instructed to share their thoughts with the interviewer. Qualitative analyses were conducted to assess whether parents and children base their answer on different experiences or reasoning, have different response styles, or interpret the items differently. RESULTS: There was discordance between parents and children, in terms of rating scale and in terms of the reasoning for their answer. Children tended to have different response styles to parents, where for example, children tended to provide extreme scores (highest or lowest score) and base their response on one single example, more than parents. Parents and children interpreted the meaning of the items very similarly. DISCUSSION: This study provides evidence to suggest that discordance among parent-child pairs on KIDSCREEN scores may be as a result of different reasoning and different response styles, rather than interpretation of items. These findings have important implications when parent-proxy reported HRQOL is used to guide clinical/treatment decisions.

In this study the psychometric properties of the Personal Optimism scale of the POSO-E questionnaire (Schweizer & Koch, 2001) for the assessment of dispositional optimism are evaluated by applying Samejima's (1969) graded response model, a parametric item response theory (IRT) model for polytomous data. Model fit is extensively evaluated via fit checks on the lower-order margins of the contingency table of observed and expected responses and visual checks of fit plots comparing observed and expected category response functions. The model proves appropriate for the data; a small amount of misfit is interpreted in terms of previous research using other measures for optimism. Item parameters and information functions show that optimism can be measured accurately, especially at moderately low to middle levels of the latent trait scale, and particularly by the negatively worded items.

We develop methods for exploiting "single-feature polymorphism" data, generated by hybridizing genomic DNA to oligonucleotide expression arrays. Our methods enable the use of such data, which can be regarded as very high density, but imperfect, polymorphism data, for genomewide association or linkage disequilibrium mapping. We use a simulation-based power study to conclude that our methods should have good power for organisms like Arabidopsis thaliana, in which linkage disequilibrium is extensive, the reason being that the noisiness of single-feature polymorphism data is more than compensated for by their great number. Finally, we show how power depends on the accuracy with which single-feature polymorphisms are called.

In many randomised trials researchers measure a continuous variable at baseline and again as an outcome assessed at follow up. Baseline measurements are common in trials of chronic conditions where researchers want to see whether a treatment can reduce pre-existing levels of pain, anxiety, hypertension, and the like. Statistical comparisons in such trials can be made in several ways. Comparison of follow up (post-treatment) scores will give a result such as "at the end of the trial, mean pain scores were 15 mm (95% confidence interval 10 to 20 mm) lower in the treatment group." Alternatively a change score can be calculated by subtracting the follow up score from the baseline score, leading to a statement such as "pain reductions were 20 mm (16 to 24 mm) greater on treatment than control." If the average baseline scores are the same in each group the estimated treatment effect will be the same using these two simple approaches. If the treatment is effective the statistical significance of the treatment effect by the two methods will depend on the correlation between baseline and follow up scores. If the correlation is low using the change score will add variation and the follow up score is more likely to show a significant result. Conversely, if the correlation is high using only the follow up score will lose information and the change score is more likely to be significant. It is incorrect, however, to choose whichever analysis gives a more significant finding. The method of analysis should be specified in the trial protocol.

Genome-wide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels. The Swedish (SE) EUROSPAN cohort (N(SE) = 656) was screened for candidate genes and the non-Swedish (NS) EUROSPAN cohorts (N(NS) = 3,282) were used for replication. In total, 3 SNPs were associated in the Swedish sample and were replicated in the non-Swedish cohorts. While SNP rs1532624 was a replication of the previously published association between CETP and HDL cholesterol, the other two were novel findings. For the latter SNPs, the p-value for association was substantially improved by inclusion of environmental covariates: SNP rs5400 (p(SE,unadjusted) = 3.6 x 10(-5), p(SE,adjusted) = 2.2 x 10(-6), p(NS,unadjusted) = 0.047) in the SLC2A2 (Glucose transporter type 2) and rs2000999 (p(SE,unadjusted) = 1.1 x 10(-3), p(SE,adjusted) = 3.8 x 10(-4), p(NS,unadjusted) = 0.035) in the HP gene (Haptoglobin-related protein precursor). Both showed evidence of association with total cholesterol. These results demonstrate that inclusion of important environmental factors in the analysis model can reveal new genetic susceptibility loci.

The purpose of this study was to validate the use of Leigh's (1990) alcohol sex expectancies scale among HIV-infected individuals presenting for treatment as a way to facilitate research on sexual risk reduction among individuals in that population. The participants were 944 men who presented for treatment at infectious disease or general medicine clinics across 8 different VA Medical Center sites. A total of 534 of these men were HIV-positive and 410 were HIV-negative. The total sample was randomly divided in half within each HIV group to form exploratory (Sample 1) and confirmatory (Sample 2) subsamples. A principal components factor analysis with oblique rotation of the original 13-item Leigh scale within each HIV group in Sample 1 revealed a 2-factor (7 and 4 items, respectively) solution that was consistent across both HIV groups. These factors were named "More Open to Sexual Pleasure" (Factor 1) and "Reduced Inhibitions about Sex (Factor 2)." A confirmatory factor analysis of the 11-item, 2-factor solution on the full Sample 2 showed a modest fit to the data, excellent internal consistency reliability of both factors, a high correlation between the factors, and strong evidence for construct validity. These results were interpreted as supporting the use of the 11-item, 2-factor version of Leigh's scale in studies of clinical samples of HIV-positive adults, and directions for research on further scale refinement are discussed.

Many statistical methods can be used to study data presented in the form of J blocks of variables observed on the same subjects. The most well-known methods are the following: Horst's generalised canonical correlation analysis, Carroll's gen- eralised canonical correlation analysis, Escofier and Page`s' multiple factor analysis and second order confirmatory factor analysis. The aim of all these methods is to identify a common structure among the J data tables. The partial least squares ŽPLS. Path modelling approach of Herman Wold can also be used on this type of data. Generalised canonical correlation analyses of Horst and Carroll and multiple factor analysis are special cases of PLS Path modelling, but this approach also leads to new useful methods. In the first part of this paper, we briefly review PLS Path modelling, then we look in greater detail at the specific case of tables without structural relations. In the second part, we have applied PLS Path modelling to a study of the cosmetic habits of women in the Ile-de-France region. Lohmo ̈ller's LVPLS software release 1.8 allowed us to carry out the application without too many difficulties.

Histologic and genetic markers can sometimes make it possible to refine a disease into subtypes. In a case-control study, an attempt to subcategorize a disease in this way can be important to elucidating its etiology if the subtypes tend to result from distinct causal pathways. Using subtyped case outcomes, one can carry out either a case-case analysis to investigate etiologic heterogeneity or do polytomous logistic regression to estimate odds ratios specific to subtypes. Unfortunately, especially when such an analysis is undertaken after the study has been completed, it may be compromised by the unavailability of tissue specimens, resulting in missing subtype data for many enrolled cases. The authors propose that one can more fully use the available data, including that provided by cases with missing subtype, by using the expectation-maximization algorithm to estimate risk parameters. For illustration, they apply the method to a study of non-Hodgkin's lymphoma in the midwestern United States. The simulations then demonstrate that, under assumptions likely to hold in many settings, the approach eliminates bias that would arise if unclassified cases were ignored and also improves the precision of estimation. Under the same assumptions, empirical confidence interval coverage is consistent with the nominal 95%.

ABSTRACT: BACKGROUND: Utilization of the natural genetic variation in traditional breeding programs remains a major challenge in crop plants. The identification of candidate genes underlying, or associated with, phenotypic trait QTLs is desired for effective marker assisted breeding. With the advent of high throughput -omics technologies, screening of entire populations for association of gene expression with targeted traits is becoming feasible but remains costly. Here we present the identification of novel candidate genes for different potato tuber quality traits by employing a pooling approach reducing the number of hybridizations needed. Extreme genotypes for a quantitative trait are collected and the RNA from contrasting bulks is then profiled with the aim of finding differentially expressed genes. RESULTS: We have successfully implemented the pooling strategy for potato quality traits and identified candidate genes associated with potato tuber flesh color and tuber cooking type. Elevated expression level of a dominant allele of the beta-carotene hydroxylase (bch) gene was associated with yellow flesh color through mapping of the gene under a major QTL for flesh color on chromosome 3. For a second trait, a candidate gene with homology to a tyrosine-lysine rich protein (TLRP) was identified based on allele specificity of the probe on the microarray. TLRP was mapped on chromosome 9 in close proximity to a QTL for potato cooking type strengthening its significance as a candidate gene. Furthermore, we have performed a profiling experiment targeting a polygenic trait, by pooling individual genotypes based both on phenotypic and marker data, allowing the identification of candidate genes associated with the two different linkage groups. CONCLUSIONS: A pooling approach for RNA-profiling with the aim of identifying novel candidate genes associated with tuber quality traits was successfully implemented. The identified candidate genes for tuber flesh color (bch) and cooking type (tlrp) can provide useful markers for breeding schemes in the future. Strengths and limitations of the approach are discussed.

Processing speed was assessed at 5, 7, and 12 months in full-term and preterm infants (birth-weight < 1,750 g). Speed was gauged directly in a new task by presenting infants with a series of paired faces, one that remained the same across trials and one that changed; trials continued until infants showed a consistent novelty preference. At all ages, preterms required about 20% more trials and 30% more time than full-terms to reach criterion. Among preterms, slower processing was associated with greater medical risk (e.g., respiratory distress syndrome). Developmental trajectories for speed (and attention) were similar for both groups. Thus, the deficits in processing speed previously found for preterms in childhood are already present in the 1st year of life.

Autism may develop in children with West syndrome. This study was conducted to determine if EEG abnormalities in patients with West syndrome predict the later onset of autism. Two groups of patients with West syndrome, older than 6 years of age, were studied. One group consisted of those with a past history of West syndrome plus autism (N=14); the control group consisted of those with a past history of West syndrome but without autism (N=14). Patients were followed at regular intervals and video-EEG recordings were done. A total of 108 (autistic group) and 123 (non-autistic group) video-EEGs were examined. The two groups were compared with respect to age, presence or absence of hypsarrhythmia, and characteristics and localization of the epileptogenic foci. chi2 and Fisher's exact tests were used. The number of patients with at least one hypsarrhythmic EEG at the age of one year or later was significantly higher in autistic subjects (86%) than in non-autistic controls (29%). The incidence of EEGs with hypsarrhythmia was also higher in the autistic group, especially in older children (autistic, 49% versus non-autistic, 18% at age 3 years and later). Frontal predominance of the primary foci on EEGs with or without hypsarrhythmia was seen in 95.3% of the autistic group and 28.8% of the non-autistic group (p=0.001). Frontal abnormalities on the EEGs, which were mainly bilateral, and the persistence of hypsarrhythmia were significantly related to the emergence of autistic behavior in patients with West syndrome. These findings suggest that paroxysmal discharges in the cortical areas undergoing rapid maturation may be involved in the development of autistic features.

Parameter estimates from analyses of univariate twin data usually do not reflect the uncertainty due to the model selection phase of the data analysis. To address the effect of model selection uncertainty on parameter estimates, we introduce frequentist model-averaged estimators for univariate twin data analysis that use information-theoretic criteria to assign model weights. We conduct simulation studies to examine the performance of model-averaged estimators of additive genetic variance, and for tests for additive genetic variance based on model-averaged estimators. In simulation studies with small or moderate sample sizes, model-averaged estimators of additive genetic variance typically have lower mean-squared error than either (i) estimators from individual twin models, or (ii) estimators obtained from a decision procedure where the best-fitting model from likelihood-ratio testing is used to estimate additive genetic variance. For each sample size simulated, bootstrap tests based on model-averaged estimators have higher power to detect additive genetic variance than currently-used tests in most cases.

AIMS: Quality of life (QoL) is an important factor of outcome tracking and treatment in alcohol misuse. A 9-item QoL scale, AlQoL 9, obtained from the generic SF 36, is proposed as a measure that characterizes the QoL of alcohol-dependent patients. Our objective was to study the psychometric properties of this subscale. METHODS: AlQoL 9 was evaluated in two study groups of patients with DSM-IV diagnosis of dependence: 104 inpatients, and 114 outpatients. Severity of dependence, alcohol consumption, psychiatric, and somatic comorbidities were assessed. We studied the global properties of AlQoL 9 and its structure. RESULTS: Cronbach alpha-coefficients in both populations indicated good internal consistency (0.71 and 0.85). Test-retest intraclass coefficients for a 2-day interval in hospital were in the range 0.57-0.78. Principal component analysis found a unidimensional scale. This subscale has properties that are consistent with the concept of QoL in alcohol dependence, i.e. lowered QoL compared with the general population, influenced by gender, and depression. CONCLUSIONS: AlQoL 9 epitomizes QoL in alcohol-dependence. It gives a global measurement with good psychometric properties. It could be used in clinical practice as a diagnosis and management support instrument and may also be useful in research for evaluating treatment efficacy.

This paper deals with the estimation of the Blood Oxygen Level-Dependent (BOLD) response to a stimulus, as measured in Functional Magnetic Resonance Imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a non-parametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called Hemodynamic Response Function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asyn- chronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly-conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an Expectation Conditional Maximization (ECM) algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

There is strong evidence for genetic influences on personality traits. Interest in one such gene, the dopamine D4 receptor (DRD4) grew after an exon III polymorphism was associated with Novelty Seeking and related measures of Extraversion. However, the findings were not confirmed in later studies. Recently, a -521C/T single nucleotide polymorphism (SNP) within the promoter region of the DRD4 gene was found to be related to Novelty Seeking scores in populations from Japan and Hungary. Since little is known about the role DRD4 plays in personality in other populations we evaluated if two DRD4 promoter SNPs, -521C/T and -616C/G, were related to personality traits in African Americans. Personality traits were measured by the NEO-FFI in 71 unrelated African Americans. Genotyping was performed using PCR-RFLP. Multivariate analyses of variance (MANOVA) were performed to evaluate the effects of gender and -616 and -521 genotypes on personality traits. A significant three-way interaction effect from gender, -616 genotype, and -521 genotype was observed for Extraversion scores (F(1,54) 5.86, P < 0.02). Subsequent analyses revealed that the association was mainly due to -521C/T genotype among females (P = 0.01). This study provides further evidence that genetic variation within the DRD4 promoter and gender differences contribute to variation in Novelty Seeking behaviors such as Extraversion.

The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha, Loevinger's scalability coefficient H, and the item cluster solution from Mokken scale analysis of the complete data were compared with the corresponding results based on the data including imputed scores. The multiple-imputation methods, two-way with normally distributed errors, corrected item-mean substitution with normally distributed errors, and response function, produced discrepancies in Cronbach's coefficient alpha, Loevinger's coefficient H, and the cluster solution from Mokken scale analysis, that were smaller than the discrepancies in upper benchmark multivariate normal imputation.

BACKGROUND: While highly active antiretroviral therapy (HAART) allows for the considerable decline in the incidence of HIV-related opportunistic infections and tumors, its effect on treating HIV infection of the brain, such as HIV-associated dementias (HADs), remains unclear. METHODS: A cross-sectional study of consecutive series of 96 patients from the Serbian HIV/AIDS cohort, treated with HAART in our HIV unit was performed to evaluate the incidence of and risk factors for cognitive/motor complex during HAART. CD4+T cell counts and pVL values at the time of neurological evaluation were parameters of the response to HAART. The mini-mental test and neurologic examination were performed at one point of time during treatment to reveal cognitive and/or motor disorders. RESULTS: After mean HAART duration of 47 months, unimpaired cognition, minor cognitive impairment, and HIV-associated dementia were recorded in 56 (58.3%), 27 (28.1%), and 13 (13.5%), respectively. Motor abnormalities had 39 (40.6%) patients. Of these, 21, 12, and 6 patients belong to the subgroups with normal cognition, minor cognitive impairment and HAD patients, respectively. Factors predictive for HAD were age over 40 (OR 3.7, 95% CI 1.07-13.28, P=0.039), and AIDS diagnosis prior to HAART initiation (OR 14.19, 95% CI 1.76-114.16, P=0.013). Conversely, factors shown to be protective against HAD were the usage of AZT and NNRTIs, as components of HAART regimens (OR 0.18, 95% CI 0.046-0.76, P=0.019, and OR 0.14, 95% CI 0.034-0.6, P=0.008). CONCLUSION: Cognitive/motor complex has still remained a significant neuropathology among late presenters and elder HIV/AIDS patients. Certain HAART regimens containing AZT, and/or NNRTIs, could be protective for these patients.

In this paper we review the problem of defining and estimating intrarater, interrater and test-retest reliability of continuous measurements. We argue that the usual notion of product-moment correlation is well adapted in a test-retest situation, whereas the concept of intraclass correlation should be used for intrarater and interrater reliability. The key difference between these two approaches is the treatment of systematic error, which is often due to a learning effect for test-retest data. We also consider the reliability of a sum and a difference of variables and illustrate the effects on components. Further, we compare these approaches of reliability with the concept of limits of agreement proposed by Bland and Altman (for evaluating the agreement between two methods of clinical measurements) and show how product-moment correlation is related to it. We then propose new kinds of limits of agreement which are related to intraclass correlation. A test battery to study the development of neuro-motor functions in children and adolescents illustrates our purpose throughout the paper.

There is consensus about the disorders that comprise the autistic spectrum, with autistic disorder, Asperger's disorder, and PDD-NOS as the most typical examples and Rett's disorder and disintegrative disorder as the other components. Important controversies regarding the precise definitions of autistic spectrum disorders and the boundaries between the milder manifestations of those disorders, particularly PDD-NOS, and non-autistic conditions have not been and cannot be resolved fully as long as there is no known biologic cause or consistent biologic or psychological marker. This includes issues as basic as whether the autistic spectrum is a predominantly unitary entity or a collection of more or less similar phenotypes with multiple, varying etiologies. This is why the highest long-term priority in the area of definite diagnosis is the search for biologic marker(s) for autism and related autism spectrum disorders [91]. In the absence of a medical test to unequivocally diagnose autism, definitions of autism and related conditions are based only on manifestations in overt behavior, with all the unreliability this entails. In the future, the discovery of biologic correlates, causes, and pathogenetic pathways will undoubtedly change the way in which autism is diagnosed and lead to a new nosology [95]. Until that time the definitions in the current versions of the classification systems should be considered in a state of evolution. The key problem of the current classification systems is the fact that the boundaries between the various disorders are fuzzy. Instead of a categorical approach, a more useful description might be that of "autistic spectrum disorder," which reflects the range of severity of symptoms. Such a dimensional understanding of PDD is useful to clinicians, who may otherwise use nonspecific terms to avoid the categorical diagnosis of autism [31]. Rutter and Schopler [96] argued for separate clinical and research schemes because clinical and research needs are different. For research purposes it is desirable to have as much direct comparability across studies as possible. The focus is on a high degree of homogeneity within diagnostic groupings. A price must be paid for this detailed specification, and the main cost lies in the proportion of cases left undiagnosed. For example, there may be good scientific reasons for a narrowly defined categorical diagnosis that includes only individuals who definitely and clearly have a specifically defined condition and excludes individuals who may have the condition. For clinicians and educators, classification helps guide the selection of treatments for an individual. From this point of view, broader diagnostic concepts may be most appropriate [95].

OBJECTIVE: This study examined the validity of child-reported exposure to secondhand smoke (SHS) and investigated factors, such as child's age, which might affect accuracy of recall. STUDY DESIGN AND SETTING: Participants were drawn from a nonprobability sample of 380 families who completed baseline assessment as part of a randomized trial of an SHS reduction intervention conducted in an urban setting in Southern California. Parents and children (aged 8-13 years) retrospectively reported child's exposure to SHS using timeline followback methodology; reports were compared with child's urine cotinine. RESULTS: Validity coefficients for parents and children were comparable (r=0.58 vs. r=0.53), but parents recalled three times more exposure than children (2.2 vs. 0.8 cigarettes per day; P<0.001). Regression models predicting cotinine indicated that including child in addition to parent reports resulted in better prediction than either alone. CONCLUSION: When there is a choice, parent reports are preferable over child reports because of decreased underreporting. However, child-reported SHS exposure had adequate validity (r>0.50) and might be appropriate in some situations. Researchers might consider collecting both parent and child reports because each made a unique contribution to the prediction of cotinine.

In data analysis, factorial methods are essential. These techniques can be used as an end in themselves, seeking to highlight underlying common factors in a group of variables. They can also be used as input to another analysis. Then, they consist in data dimension reduction and operate by replacing the original variables, sometimes highly correlated, by a smaller number of linearly independent variables. Factor Analysis (F.A.) is one possible method for quantitative data. This article aims at presenting in a synthetic way the F.A. model, rarely described in French books, but frequent in the Anglo-Saxon literature, and often available in softwares. The presentation of the estimation techniques for the F.A. model enables to estab- lish the existing connection between Principal Component Analysis (P.C.A.) and F.A. The usefulness of rotation techniques, which can facilitate the interpretation of the results, will also be shown. An application on crime data of American cities will be carried out and will allow to describe the results provided by three of the most used statistical softwares : SAS, SPAD and SPSS. Then it will help to clarify the vocabulary, sometimes confused for the user.

BACKGROUND: Patterns of comorbidity suggest that the common psychiatric and substance use syndromes may be divisible into 2 broad groups of internalizing and externalizing disorders. We do not know how genetic and environmental risk factors contribute to this pattern of comorbidity or whether the etiologic structure of these groups differ in men and women. METHODS: Lifetime diagnoses for 10 psychiatric syndromes were obtained at a personal interview in more than 5600 members of male-male and female-female twin pairs ascertained from a population-based registry. Multivariate twin modeling was performed using the program Mx. RESULTS: We first fit models to the following 7 syndromes: major depression, generalized anxiety disorder, phobia, alcohol dependence, drug abuse/dependence, adult antisocial behavior, and conduct disorder. The full model, which could be constrained to equality in male and female subjects, identified 2 genetic factors. The first had strongest loadings on alcohol dependence, drug abuse/dependence, adult antisocial behavior, and conduct disorder; the second, on major depression, generalized anxiety disorder, and phobia. Alcohol dependence and drug abuse/dependence had substantial disorder-specific genetic risk factors. Shared environmental factors were most pronounced for conduct disorder and adult antisocial behavior. No clear internalizing/externalizing structure was seen for the unique environmental common factors. We then fit models to 5 internalizing syndromes. The full model, which could also be constrained to equality in men and women, revealed one genetic factor loading most heavily on major depression and generalized anxiety disorder and another loading most strongly on animal and situational phobia. CONCLUSIONS: The underlying structure of the genetic and environmental risk factors for the common psychiatric and drug abuse disorders in men and women is very similar. Genetic risk factors predispose to 2 broad groups of internalizing and externalizing disorders. Within the internalizing disorders, 2 genetic factors are seen that predispose to disorders dominated by anxious-misery and fear. Substance use disorders have disorder-specific genetic risks. The externalizing disorders of conduct disorder and adult antisocial behavior are significantly influenced by the shared environment. The pattern of lifetime comorbidity of common psychiatric and substance use disorders results largely from the effects of genetic risk factors.

Personality traits are a variance of behavioral patterns among individuals and may reflect a variance of brain activity, but their neurobiological explanation is still a matter of debate. Cloninger proposed three dimensions of personality traits, each of which has strong correlation with activity in a specific central monoaminergic system. Although this theory has been supported by physiological and genetic studies, it is still unclear how these personality parameters are correlated with the activity of the cortical networks which control human behavior. Here we measured the regional cerebral blood flow (rCBF) at rest in 30 normal volunteers who completed the personality inventory of Cloninger. Voxel-by-voxel analysis was employed to identify cortical regions where the rCBF showed significant correlation with any of the three personality parameters. Statistically significant correlation was observed in several paralimbic and neocortical regions and was consistent with the assumed monoaminergic influence on neural activity and the distribution of its projections, in each personality dimension. The results suggest that activity in a variety of cortical regions is associated with human personality traits and lend support to Cloninger's theory concerning central monoaminergic influence on human personality traits.

Recognition memory experiments are an important source of empirical constraints for the- ories of memory. Unfortunately, standard methods for analyzing recognition memory data have problems that are often severe enough to prevent clear answers being obtained. A key example is whether longer lists lead to poorer recognition performance. The presence or absence of such a list-length effect is a critical test of competing item- and context-noise based theories of interference and bares on whether recognition involves “recall-like” com- ponents as dual process theories would contend. However, the issue has remained unre- solved, in part, because of the weaknesses of the standard analysis. In this paper, we develop a Bayesian method of analysis and apply it to new data on the list-length effect. The analysis allows us to find positive evidence in favor of a null list-length effect as pre- dicted by context noise models. The data also illustrate the importance of the contextual reinstatement process on recognition performance and show how previous work demon- strating a list-length effect may have been contaminated by reinstatement confounds. By contrasting our new method against the standard approach we highlight the advantages of the Bayesian framework when inferring the values of psychologically meaningful vari- ables, and in choosing between models representing different theoretical assumptions about memory.

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator.

ABSTRACT: BACKGROUND: The Sense of Coherence (SOC) scale is assumed to measure a distinct salutogenic construct separated from measures of anxiety and depression. Our aim was to challenge this concept. METHOD: The SOC-scale, Beck's Depression Inventory (BDI), Beck's Anxiety Inventory (BAI), the emotional subscale of the Strengths and Difficulties Questionnaire (SDQ-em) and self-assessed health-related and physiological parameters were collected from a sample of non-clinical adolescent females (n=66, mean age 16.5 years with a range of 15.9-17.7 years) and from female psychiatric patients (n=73), mean age 16.8 years with a range of 14.5-18.4 years), with diagnoses of major depressive disorders (MDD) and anxiety disorders. RESULTS: The SOC scores showed high inverse correlations to BDI, BAI and SDQ-em. In the non-clinical sample the correlation coefficient was -0.86 to -0.73 and in the clinical sample -0.74 to -0.53 (p<0.001). Multiple regression models showed that BDI was the strongest predictor of SOC in the non-clinical (beta coefficient -0.47) and clinical sample (beta coefficient -0.52). The total degree of explanation of self-assessed anxiety and depression on the SOC variance estimated by multiple R2=0.74, adjusted R2=0.73 in the non-clinical sample and multiple R2=0.66, adjusted R2=0.65 in the clinical sample. Multivariate analyses failed to isolate SOC as a separate construct and the SOC-scale, BDI, BAI and SDQ-em showed similar patterns of correlations to self-reported and physiological health parameters in both samples. The SOC-scale was the most stable measure over six months. CONCLUSIONS: The SOC-scale did not appear to be a measure of a distinct salutogenic construct, but an inverse measure of persistent depressive symptoms and generalized social anxiety similar to the diagnostic criteria for major depressive disorder (MDD), dysthymic disorder, generalized anxiety disorder (GAD) or generalized social anxiety disorder (SAD) according to DSM-IV. These symptoms were better captured with SOC than by the specialized scales for anxiety and depression. Self-assessment scales that adequately identify MDD, dysthymic disorder, GAD and SAD need to be implemented. Comorbidity of these disorders is common in adolescent females and corresponds to a more severe symptomatology and impaired global function.

Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of su- pervised learning and have been studied extensively. However, these two issues seem to have been investi- gated separately as two independent problems. If train- ing input points and models are simultaneously opti- mized, the generalization performance would be further improved. In this paper, we propose a new approach called ensemble active learning for solving the problems of active learning and model selection at the same time. We demonstrate by numerical experiments that the pro- posed method compares favorably with alternative ap- proaches such as iteratively performing active learning and model selection in a sequential manner.

Dopamine plays an important role in modulating incentive motivation, expressed behaviorally as approach behavior. EEG studies report association between approach behavior and asymmetric pattern of activation in anterior cortical regions (as measured by the inverse of EEG alpha power). Therefore, individual differences in incentive motivation may reflect asymmetries in dopaminergic systems. We examined this hypothesis by studying the relationship between self-reported degree of incentive motivation, and asymmetry of D2 receptor availability in healthy volunteers. Nineteen healthy participants were studied with positron emission tomography (PET) and [11C]raclopride to assess the availability of dopamine D2 receptors in left and right striatum. Incentive motivation was assessed by the Achievement scale of the Multidimensional Personality Questionnaire. The Achievement score was negatively correlated with the Asymmetry Index ([R-L]/[R+L]) of D2 receptor availability (r=-.721, p=.001), suggesting that greater positive incentive motivation is associated with higher receptor availability in the left relative to the right hemisphere.

SCOPE OF THE REPORT: This report is concerned with the evaluation of measures broadly designed to measure quality of life (QoL) in children and adolescents, either by self-report or proxy raters. Four research questions were identified: (1) To what extent are adult measures used in the evaluation of healthcare interventions in children? (2) How appropriate are adult measures for use with children? (3) To what extent do child self-reports correspond with assessments made by parents and carers? (4) How feasible and reliable are proxy measures of QoL in different disease contexts? OBJECTIVES: (1) To review the state of the art with regard to measurement of QoL for children. (2) To make recommendations regarding the value of currently available measures for different purposes. (3) To identify further research needs. METHOD: Electronic databases were searched for the period 1980-July 1999 for articles relating to measures of QoL, health status or well-being in children (under 18 years) with chronic disease. Handsearching of relevant journals and cross-referencing with reference lists in identified articles was also carried out. Key workers in the field were contacted for additional information, and the Internet was searched for relevant websites. RESULTS: Forty-three measures were identified (19 generic and 24 disease-specific). Sixteen measures allowed for completion by children and parent/caregiver; seven only allowed for completion by a proxy, and the remainder (n = 17) allowed only for child completion. The measures were described as QoL (n = 30), health status, (n = 2), perception of illness (n = 1), life satisfaction (n = 1) and quality of well-being (n = 1). RESULTS - TO WHAT EXTENT ARE ADULT MEASURES USED IN THE EVALUATION OF HEALTHCARE INTERVENTIONS IN CHILDREN?: Three studies were identified where adult measures were used with very few changes made for children. In 11 studies involving nine separate measures of QoL, adult measures were used as a model for work with children. RESULTS - HOW APPROPRIATE ARE ADULT MEASURES FOR USE WITH CHILDREN?: Adult measures may fail to tap the specific aspects of QoL that are important to the child. Measures based on adult work impose considerable response burden for children, in terms of length, reading skills and response scale. Wording and format of adult measures may need to be modified to account for children's cognitive and language skills. More basic research is needed to determine the level of response burden that children of different ages can manage. Assessments of difficulty (e.g. reading age) need to be routinely included with information about new measures. RESULTS - TO WHAT EXTENT DO CHILD SELF-REPORTS CORRESPOND WITH ASSESSMENTS MADE BY PARENTS AND CARERS?: Fourteen studies were identified in which concor-dance between child and parent was investigated, often as part of the development of a new measure. There was some evidence for greater concordance between child and parent for physical functioning compared with social and emotional domains, but greater heterogeneity in the latter measures may contribute to inconsistent results. There was no simple relationship between concordance and moderating variables such as age, gender and illness, but this conclusion was addressed only very rarely. RESULTS - HOW FEASIBLE AND RELIABLE ARE PROXY MEASURES OF QOL IN DIFFERENT DISEASE CONTEXTS?: Only five papers fulfilled the review criteria. Evaluation is difficult because authors fail to justify their choice of measures, and do not report critical information such as completion rates or missing data. Use of existing measures can potentially eliminate the time and expense required to develop a comprehensive measure of QoL, but a full battery of standardised tests may be expensive in terms of time for administration and scoring. In addition, battery measures tend to be lengthy and therefore demanding for sick patients. They are not recommended for work with children. RECOMMENDATIONS FOR RESEARCH - MINIMUM CRITERIA FOR NEW MEASURES: A set of procedures needs to be established for the development of new measures. These need to draw on the experience gained in development of child and adult measures to date. Basic research to enhance understanding of how children interpret questions in QoL measures is recommended. We need to understand the differences in meaning of items between children and adults, and between children of different ages. Some attempt to develop measures for children of 6 years or more have been reported, and these should be further developed. (ABSTRACT TRUNCATED)

Some BALB/c substrains exhibit different levels of aggression. We compared aggression levels between male BALB/cJ and BALB/cByJ substrains using the resident intruder paradigm. These substrains were also assessed in other tests of emotionality and information processing including the open field, forced swim, fear conditioning, and prepulse inhibition tests. We also evaluated single nucleotide polymorphisms (SNPs) previously reported between these BALB/c substrains. Finally, we compared BALB/cJ and BALB/cByJ mice for genomic deletions or duplications, collectively termed copy number variants (CNVs), to identify candidate genes that might underlie the observed behavioral differences. BALB/cJ mice showed substantially higher aggression levels than BALB/cByJ mice; however, only minor differences in other behaviors were observed. None of the previously reported SNPs were verified. Eleven CNV regions were identified between the two BALB/c substrains. Our findings identify a robust difference in aggressive behavior between BALB/cJ and BALB/cByJ substrains, which could be the result of the identified CNVs.

Although a substantial body of epidemiological and economic literature on asthma exists, relatively little is known about the impact of asthma on health-related quality of life (HRQL). The purpose of this review was to synthesize results from recent studies, profile the factors influencing HRQL in asthmatics, discuss the impact of treatment on HRQL outcomes, and offer recommendations for further research. The results of this review support the premise that asthma can adversely affect the physical, psychological, and social domains of HRQL. Published data suggest that females, those from lower socioeconomic groups, and ethnic minorities experience poorer quality of life as a result of their asthma symptoms. Results of published clinical trials indicate treatment regimens can have a significant impact on HRQL outcomes. Pharmacological interventions appear to effect change primarily in the physical domain and behavioral interventions lead to improvements in both physical and psychosocial domains. Future research should focus on precise a priori delineation of research hypotheses, including the selection of primary and secondary endpoints, the clarification and consistent application of criteria for defining asthma severity, thoughtful selection of HRQL instruments appropriate for the research hypotheses and target population, and careful delineation of clinically meaningful change scores of asthma-specific outcome measures.

This paper discusses the role of primal and (Lagrange) dual model representations in problems of supervised and unsupervised learning. The specification of the estimation problem is conceived at the primal level as a constrained optimization problem. The constraints relate to the model which is expressed in terms of the feature map. From the conditions for optimality one jointly finds the optimal model representation and the model estimate. At the dual level the model is expressed in terms of a positive definite kernel function, which is characteristic for a support vector machine methodology. It is discussed how least squares support vector machines are playing a central role as core models across problems of regression, classification, principal component analysis, spectral clustering, canonical correlation analysis, dimensionality reduction and data visualization.

BACKGROUND: Our previous work described the neural processes of motor response inhibition during a stop signal task (SST). Employing the race model, we computed the stop signal reaction time (SSRT) to index individuals' ability in inhibitory control. The pre-supplementary motor area (preSMA), which shows greater activity in individuals with short as compared to those with long SSRT, plays a role in mediating response inhibition. In contrast, the right inferior prefrontal cortex (rIFC) showed greater activity during stop success as compared to stop error. Here we further pursued this functional differentiation of preSMA and rIFC on the basis of an intra-subject approach. RESULTS: Of 65 subjects who participated in four sessions of the SST, we identified 30 individuals who showed a difference in SSRT but were identical in other aspects of stop signal performance between the first ("early") and last two ("late") sessions. By comparing regional brain activation between the two sessions, we confirmed greater preSMA but not rIFC activity during short as compared to long SSRT session within individuals. Furthermore, putamen, anterior cerebellum and middle/posterior cingulate cortex also showed greater activity in association with short SSRT. CONCLUSION: These results are consistent with a role of medial prefrontal cortex in controlled action and inferior frontal cortex in orienting attention. We discussed these findings with respect to the process of attentional monitoring and inhibitory motor control during stop signal inhibition.

Item response theory (IRT) has a number of potential advantages over classical test theory in assessing self-reported health outcomes. IRT models yield invariant item and latent trait estimates (within a linear transformation), standard errors conditional on trait level, and trait estimates anchored to item content. IRT also facilitates evaluation of differential item functioning, inclusion of items with different response formats in the same scale, and assessment of person fit and is ideally suited for implementing computer adaptive testing. Finally, IRT methods can be helpful in developing better health outcome measures and in assessing change over time. These issues are reviewed, along with a discussion of some of the methodological and practical challenges in applying IRT methods.

The purpose of this study was to examine whether there are advantages in terms of outcome assessment of using Rasch methods of scoring the 12-item Oxford Hip Score (OHS) questionnaire over conventionally Likert scores. As part of a prospective cohort study of total hip replacements in five former regions of England the OHS was sent to patients pre-operatively, at 3 months and 1 year post-operatively. Post-operative data was collected on over 5000 cases. Based on the level of satisfaction with surgery, patients were divided into satisfied and dissatisfied. Analyses were performed to test the relative precision (RP) of Rasch scoring vs. conventionally Likert scores in discriminating the groups experiencing different level of satisfaction. Considerable gains in precision were achieved with Rasch scoring methods when groups were compared 3 and 12 months post-operatively. The results from the current study suggest that in some situations there may be substantial gains in measuring health related outcomes using Rasch-based scoring methods.

PURPOSE: To determine whether developmental care interventions reduce neurodevelopmental delay, poor weight gain, length of hospital stay, length of mechanical ventilation, physiologic stress, and other clinically relevant adverse outcomes in preterm infants. SUBJECTS: Infants born at less than 37 weeks postconceptional age. This review consisted of 31 studies in 4 categories of developmental care interventions, 19 subgroups, and multiple clinical outcomes. The total sample sizes in the individual studies ranged from 16 to 259; the sample size in 18 of the studies was less than 50. DESIGN AND METHODS: A systematic review, based on the Cochrane Collaboration format, of all randomized trials in which elements of developmental care are compared with routine nursery care and that measured clinically relevant outcomes. Searches were made of MEDLINE from 1966 to July 2000. Additional databases were also searched electronically. Reference lists and bibliographies of relevant articles were hand-searched. Experts in the field were contracted. If more than one study in an outcome category existed, a meta-analysis was conducted. PRIMARY OUTCOME MEASURES: Outcome measures included the following: length of hospital stay, weight at discharge, neurodevelopment, physiologic parameters, feeding growth, sleep/wake states, age at discharge, neonatal outcomes, cost of hospital stay, and death. PRINCIPAL RESULTS: Developmental care interventions showed some benefit to preterm infants with respect to improved short-term growth outcomes, decreased respiratory support, decreased incidence of moderate to severe chronic lung disease, decreased length and cost of hospital stay, and improved neurodevelopmental outcomes to 24 months corrected age. These findings were based on 2 or 3 small trials for each outcome. Although a number of other benefits were shown, those results were from single studies with small sample sizes. The lack of blinding of the assessors of the outcome variables was a significant methodological flaw in half of the studies. The costs of the interventions and personnel were not considered in any of the studies. CONCLUSIONS: In most studies, the inclusion of multiple interventions made the determination of the effect of any single intervention difficult. Although there is evidence of some benefit of developmental care interventions overall and no major harmful effects reported, there were a large number of outcomes for which no or conflicting effects were shown. The single trials that did show a significant effect of an intervention on a major clinical outcome were based on small sample sizes, and the findings often were not supported in other small trials. Before a clear direction for practice can be supported, evidence showing more consistent effects of developmental care interventions on important short- and long-term clinical outcomes is needed. The economic impact of the implementation and maintenance of developmental care practices should be considered by individual institutions.

BACKGROUND: This study aimed at examining the relationship between parental subjective health status and adolescents' health-related quality of life (HRQoL) as well as the role of gender, socioeconomic status, presence of chronic health care needs and social support on the above interaction. METHODS: Questionnaires were administered to a Greek nation-wide random sample of adolescents (N = 1,194) aged 11-18 years and their parents (N = 973) in 2003. Adolescents' and parents' status was assessed, together with reports of socio-economic status and level of social support. Various statistical tests were used to determine the extent to which these variables were related to each other. RESULTS AND DISCUSSION: Parental subjective mental health status was significantly correlated with adolescents' better physical and psychological wellbeing, moods and emotions, parent-child relationships, school environment and financial resources. Parental subjective physical health status was strongly associated with more positive adolescents' self-perception. Adolescents' male gender, younger age, absence of chronic health care needs, high social support, and higher family income were positively associated with better HRQoL. CONCLUSIONS: This study reinforces the importance of parental subjective health status, along with other variables, as a significant factor for the adolescents' HRQoL.

ABSTRACT Objective: Statins have been shown to reduce the risk of major cardiovascular disease. We recognize that there is a major gap between the use of statins in actual practice and treatment guidelines for dyslipidemia. Low adherence to statins may have a significant impact on clinical issues and health-care costs. The objective is to evaluate the impact of low adherence to statins on clinical issues and direct health-care costs. Methods: A cohort of 55,134 patients newly treated with statins was reconstructed from the Régie de l'Assurance Maladie du Québec and Med-Echo databases. Subjects included were aged between 45 and 85, initially free of cardiovascular disease, newly treated with statins between 1999 and 2002, and followed-up for a minimum of 3 years. Adherence to statins was measured in terms of the proportion of days' supply of medication dispensed over a defined period, and categorized as >/=80% or <80%. The adjusted odds ratio (OR) of cardiovascular events between the two adherence groups was estimated using a polytomous logistic analysis. The mean costs of direct health-care services were evaluated. A two-part model was applied for hospitalization costs. Results: The mean high adherence level to statins was around to 96% during follow-up; and this value was at 42% for the low adherence level. The patients with low adherence to statins were more likely to have coronary artery disease (OR 1.07; 95% confidence interval [CI], 1.01-1.13), cerebrovascular disease (OR 1.13; 95% CI 1.03-1.25), and chronic heart failure within 3-year period of follow-up (OR 1.13; 95% CI 1.01-1.26). Low adherence to statins was also associated with an increased risk of hospitalization by 4% (OR 1.04; 95% CI 1.01-1.09). Among patients who were hospitalized, low adherence to statins was significantly associated with increase of hospitalization costs by approximately $1060/patient for a 3-year period. Conclusion: Low adherence to statins was correlated with a higher risk of cardiovascular disease, hospitalization rate, and hospitalization costs. An increased level of adherence to statins agents should provide a better health status for individuals and a net economic gain.

BACKGROUND: This is a predictive validity study examining the extent to which developmental vulnerability at kindergarten entry (as measured by the Early Development Instrument, EDI) is associated with children's basic skills in 4th grade (as measured by the Foundation Skills Assessment, FSA). METHODOLOGY/PRINCIPAL FINDINGS: Relative risk analysis was performed on a large database linking individual-level EDI ratings to the scores the same children obtained on a provincial assessment of academic skills (FSA-Foundation Skills Assessment) four years later. We found that early vulnerability in kindergarten is associated with the basic skills that underlie populations of children's academic achievement in reading, writing and math, indicating that the Early Development Instrument permits to predict achievement-related skills four years in advance. CONCLUSIONS/SIGNIFICANCE: The EDI can be used to predict children's educational trends at the population level and can help select early prevention and intervention programs targeting pre-school populations at minimum cost.

The R package clValid contains functions for validating the results of a clustering analysis. There are three main types of cluster validation measures available, “inter- nal”, “stability”, and “biological”. The user can choose from nine clustering algorithms in existing R packages, including hierarchical, K-means, self-organizing maps (SOM), and model-based clustering. In addition, we provide a function to perform the self-organizing tree algorithm (SOTA) method of clustering. Any combination of validation measures and clustering methods can be requested in a single function call. This allows the user to si- multaneously evaluate several clustering algorithms while varying the number of clusters, to help determine the most appropriate method and number of clusters for the dataset of interest. Additionally, the package can automatically make use of the biological informa- tion contained in the Gene Ontology (GO) database to calculate the biological validation measures, via the annotation packages available in Bioconductor. The function returns an object of S4 class “clValid”, which has summary, plot, print, and additional methods which allow the user to display the optimal validation scores and extract clustering results.

Prevalence rates for psychiatric disorders in the elderly are presented from the initial cross-sectional stage of a longitudinal community study of the incidence of dementia in the city of Liverpool. Together with five other centres in the UK the MRC-ALPHA project forms part of the MRC multicentre incidence study of dementia and cognitive decline. An age- and sex-stratified random sample of 5222 subjects aged 65 was interviewed at home using the Geriatric Mental State-AGECAT package to provide computer diagnoses. The overall age-standardized prevalence rates for organic disorder (4.7%) depressive illness (10.0%) and the neuroses (2.5%) are consistent with levels found in previous smaller studies that have used MS-AGECAT. Each of these diagnoses is more common in females than males. A rise in organic disorder with age is confirmed as continuing into the oldest age groups for both sexes. An apparent decline with age observed for depression and neurosis diagnoses disappears when organic cases are excluded from the analysis.

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model, nonnormal continuous measures, and nonlinear relationships among observed and/or latent variables. When the objective of a SEMM analysis is the identification of latent classes, these conditions should be considered as alternative hypotheses and results should be interpreted cautiously. However, armed with greater knowledge about the estimation of SEMMs in practice, researchers can exploit the flexibility of the model to gain a fuller understanding of the phenomenon under study.

BACKGROUND: The Mood and Anxiety Symptom Questionnaire (MASQ) was designed to specifically measure the Tripartite model of affect and is proposed to offer a delineation between the core components of anxiety and depression. Factor analytic data from adult clinical samples has shown mixed results; however no studies employing confirmatory factor analysis (CFA) have supported the predicted structure of distinct Depression, Anxiety and General Distress factors. The Tripartite model has not been validated in a clinical sample of older adolescents and young adults. The aim of the present study was to examine the validity of the Tripartite model using scale-level data from the MASQ and correlational and confirmatory factor analysis techniques. METHODS: 137 young people (M = 17.78, SD = 2.63) referred to a specialist mental health service for adolescents and young adults completed the MASQ and diagnostic interview. RESULTS: All MASQ scales were highly inter-correlated, with the lowest correlation between the depression- and anxiety-specific scales (r = .59). This pattern of correlations was observed for all participants rating for an Axis-I disorder but not for participants without a current disorder (r = .18). Confirmatory factor analyses were conducted to evaluate the model fit of a number of solutions. The predicted Tripartite structure was not supported. A 2-factor model demonstrated superior model fit and parsimony compared to 1- or 3-factor models. These broad factors represented Depression and Anxiety and were highly correlated (r = .88). CONCLUSION: The present data lend support to the notion that the Tripartite model does not adequately explain the relationship between anxiety and depression in all clinical populations. Indeed, in the present study this model was found to be inappropriate for a help-seeking community sample of older adolescents and young adults.

Kyngdon argues that psychometricians have erroneously claimed the Rasch model to be an instance of representational measurement, because the Rasch model does not map a bona fide empirical relational system (ERS) into a numerical relational system (NRS). While we agree that one does not automatically achieve a conjoint measurement representation upon fitting a Rasch model, we do not agree that the Rasch model could not in principle yield such a representation. In our view, whether this is possible depends on what one is prepared to accept as an empirical relational system. This is a philosophical question that extends beyond the scope of the formal struc- tures advanced in representationalism and psychometrics; a question, more- over, that is not currently settled. We examine some of the ways in which one may react to this question, and conclude that Kyngdon's argument depends on a specific, and perhaps too strong, interpretation of representa- tionalism and psychometric models.

Alcohol dependence is characterized by frequent, compulsive and uncontrolled consumption of alcohol associated with behavior of maladaption and destruction. It is an etiologically and clinically heterogeneous syndrome, moderately to highly heritable, and caused by interaction of genes and environment. Alcohol dependence is related to other psychiatric diseases by common neurobiological pathways, including those that modulate reward, behavioral control as well as anxiety and stress response. Alcohol induces adaptive changes in brain function providing the basis for tolerance, craving, withdrawal, and emotional disturbance. The differentiation of psychobiological traits of addictive behavior reflecting neurobiological processes is therefore of particular importance for the dissection of the complex genetic susceptibility to alcohol dependence. A central serotonin (5-HT) deficit is thought to be involved in the pathogenesis of alcohol dependence by modulating motivational behavior, neuroadaptive processes, and resulting emotional disturbance. 5-HT-related impulsive, aggressive, and suicidal behavior has been linked to a primordial personality that is susceptible to alcohol dependence. Although variations in many of the genes that encode receptors, enzymes, and transporters of the 5-HT system have been tested as risk factors for alcohol dependence, genetic analyses of 5-HT signaling in alcohol dependence have mainly been focused on the 5-HT transporter (5-HTT) gene. Due to its central role in the fine-tuning serotonergic neurotransmission, a regulatory variant of the 5-HTT, which is associated with anxiety related traits, is not only a key player in the neurobiological mechanism of gene x environment interaction in the etiology of depression, but also contributes to the risk to develop alcohol dependence with antisocial behavior and suicidality. Evidence for a modulatory effect of allelic variation of 5-HTT function on limbic circuit responses to emotional stimuli suggests that genotype-endophenotype correlations may be accessible to molecular functional imaging of the brain. These new developments have broad implications for our understanding how genetic vulnerability to alcohol dependence is manifested in the brain's response to emotional stimuli.

We studied costs of healthcare and productivity loss in 487 German outpatients starting anthroposophic treatment: Group 1 was treated for depression, Group 2 had depressive symptoms but were treated for another chronic disorder, while Group 3 did not have depressive symptoms. Costs were adjusted for socio-demographics, comorbidity, and baseline health status. Total costs in groups 1-3 averaged euro7,129, euro4,371, and euro3,532 in the pre-study year (P = 0.008); euro6,029, euro3,522, and euro3,353 in the first year (P = 0.083); and euro4,929, euro3,792, and euro4,031 in the second year (P = 0.460). In the 2nd year, costs were significantly reduced in Group 1. This study underlines the importance of depression for health costs, and suggests that treatment of depression could be associated with long-term cost reductions.

This paper illustrates new hybrid latent variable models that are promising for phenotypical analyses. The hybrid models combine features of dimensional and categorical analyses seen in the conventional techniques of factor analysis and latent class analysis. The paper focuses on the analysis of categorical items, which presents especially challenging analyses with hybrid models and has recently been made practical in the Mplus program. The hybrid models are typically seen to fit data better than conventional models of factor analysis (IRT) and latent class analysis. An illustration is given in the form of analysis of tobacco dependence in a general population survey.

Understanding the genetic structure of human populations is of fundamental interest to medical, forensic and anthropological sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation and suggest the potential to use large samples to uncover variation among closely spaced populations. Here we characterize genetic variation in a sample of 3,000 European individuals genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, the results are relevant to the prospects of genetic ancestry testing; an individual's DNA can be used to infer their geographic origin with surprising accuracy-often to within a few hundred kilometres.

OBJECTIVE: We evaluate the effects of mode and order of administration on health-related quality of life (HRQOL) scores. METHOD: We analyzed HRQOL data from the Clinical Outcomes and Measurement of Health Study (COMHS). In COMHS, we enrolled patients with heart failure or cataracts at three sites (University of California, San Diego, University of California, Los Angeles, and University of Wisconsin). Patients completed self-administered HRQOL instruments at baseline and months 1 and 6 post-baseline, including the EuroQol (EQ-5D), Health Utilities Index (HUI), Quality of Well-Being Scale-self-administered (QWB-SA), and the Short Form (SF)-36v2. At the 6 months follow-up, individuals were randomized to mail or telephone administration first, followed by the other mode of administration. We used repeated measures mixed effects models, adjusting for site, patient age, education, gender, and race. RESULTS: Included were 121 individuals entering a heart failure program and 326 individuals scheduled for cataract surgery who completed the survey by mail or phone at the 6-month follow-up. The majority of the sample was female (53%) and white (86%). About a quarter of the sample had high school education or less (26%). The average age was 66 (36-91 range). HRQOL scores were higher (more positive) for phone administration following mail administration. The largest differences in scores between phone and mail responses occurred for comparisons of telephone responses for those who were randomized to a mail survey first compared with mail responses for those randomized to a telephone survey first (i.e., mode effects for responses that were given on the second administration of the HRQOL measures). The QWB-SA was the only measure that did not display the pattern of mode effects. The biggest differences between modes were 4 points on the SF-36v2 physical health and mental health component summary scores, 0.06 on the SF-6D, 0.03 on the QWB-SA, 0.08 on the EQ-5D, 0.04 on the HUI2, and 0.10 on the HUI3. CONCLUSIONS: Telephone administration yields significantly more positive HRQOL scores for all of the generic HRQOL measures except for the QWB-SA. The magnitude of effects was clearly important, with some differences as large as a half-standard deviation. These findings confirm the importance of considering mode of administration when interpreting HRQOL scores.

Anorexia nervosa is a severe disorder which seems likely to have a multifactorial aetiology. However, several studies have stressed that genetic factors play a significant role. Epidemiological studies have shown that the lifetime risk for first-degree relatives of a patient with an eating disorder is 6% compared to 1% among relatives of controls, and a twin study performed on 34 pairs of twins has shown a higher concordance rate in monozygotic twins (55%) compared to dizygotic twins (7%). The vulnerability component of anorexia nervosa that can be attributed to genetic influences has been estimated from twin studies to be around 70%. Despite this, few genetic studies have been performed testing the role of candidate genes which code for proteins potentially implicated in the aetiopathogenesis of the disorder. In this review, genes encoding components of the dopamine, serotonin, opiate, and noradrenaline systems are assessed for their role in anorexia nervosa. Attention is paid to psychological dimensions, clinical symptoms, co-morbidity frequency, pharmacological data, and biological measures that characterize anorexia nervosa.

Patient relevant outcomes, measured using questionnaires, are becoming increasingly popular endpoints in randomized clinical trials (RCTs). Recently, interest in the use of item response theory (IRT) to analyze the responses to such questionnaires has increased. In this paper, we used a simulation study to examine the small sample behavior of a test statistic designed to examine the difference in average latent trait level between two groups when the two-parameter logistic IRT model for binary data is used. The simulation study was extended to examine the relationship between the number of patients required in each arm of an RCT, the number of items used to assess them, and the power to detect minimal, moderate, and substantial treatment effects. The results show that the number of patients required in each arm of an RCT varies with the number of items used to assess the patients. However, as long as at least 20 items are used, the number of items barely affects the number of patients required in each arm of an RCT to detect effect sizes of 0.5 and 0.8 with a power of 80%. In addition, the number of items used has more effect on the number of patients required to detect an effect size of 0.2 with a power of 80%. For instance, if only five randomly selected items are used, it is necessary to include 950 patients in each arm, but if 50 items are used, only 450 are required in each arm. These results indicate that if an RCT is to be designed to detect small effects, it is inadvisable to use very short instruments analyzed using IRT. Finally, the SF-36, SF-12, and SF-8 instruments were considered in the same framework. Since these instruments consist of items scored in more than two categories, slightly different results were obtained.

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are both highly heritable neurodevelopmental disorders. Evidence indicates both disorders co-occur with a high frequency, in 20-50% of children with ADHD meeting criteria for ASD and in 30-80% of ASD children meeting criteria for ADHD. This review will provide an overview on all available studies [family based, twin, candidate gene, linkage, and genome wide association (GWA) studies] shedding light on the role of shared genetic underpinnings of ADHD and ASD. It is concluded that family and twin studies do provide support for the hypothesis that ADHD and ASD originate from partly similar familial/genetic factors. Only a few candidate gene studies, linkage studies and GWA studies have specifically addressed this co-occurrence, pinpointing to some promising pleiotropic genes, loci and single nucleotide polymorphisms (SNPs), but the research field is in urgent need for better designed and powered studies to tackle this complex issue. We propose that future studies examining shared familial etiological factors for ADHD and ASD use a family-based design in which the same phenotypic (ADHD and ASD), candidate endophenotypic, and environmental measurements are obtained from all family members. Multivariate multi-level models are probably best suited for the statistical analysis.

OBJECTIVES: We investigated differences between foreign- and U.S.-born Asian Americans in self-rating their physical and mental health. In particular, we tested whether the foreign-born respondents underreport the extreme categories of the scale as compared with U.S.-born respondents. METHODS: We analyzed data from the National Latino and Asian American Study to examine whether immigrants are less likely to use the extreme ends of the 5-category self-rated health scales than their U.S.-born counterparts. We used propensity score matching to derive groups of U.S.- and foreign-born Asian Americans who share similar demographic and health characteristics. We defined propensity scores as predicted probabilities of being U.S. born, given individual background characteristics. The propensity score framework allowed us to make descriptive comparisons of self-rated health responses controlling for background characteristics. We used log-linear symmetry models to examine cross-tabulations of self-rated physical and mental health reports in matched pairs by the 2 (extreme and nonextreme) and 5 ("excellent," "very good," "good," "fair," and "poor") categories. RESULTS: Controlling for background characteristics, we found no evidence that foreign-born Asian Americans are less likely to endorse extreme categories in self-rated physical or mental health than U.S.-born Asian Americans, as well as no evidence of imbalances in endorsement of any particular self-rated health category between the 2 groups. CONCLUSIONS: Controlling for demographic and health characteristics, we find no systematic differences between foreign- and U.S.-born Asian Americans in reporting self-rated physical and mental health on the 5-category scales from "excellent" to "poor."

Recent studies have demonstrated the importance of sex effects on the underlying genetic architecture of insulin-related traits. To explore sex-specific genetic effects on fasting insulin, we tested for genotype-by-sex interaction and conducted linkage analysis of fasting insulin in Hypertension Genetic Epidemiology Network families. Hypertensive siblings and their first-degree relatives were recruited from five field centers. We performed a genome scan for quantitative trait loci influencing fasting insulin among 1,505 European Americans and 1,616 African Americans without diabetes. Sex-stratified linear regression models, adjusted for race, center, and age, were explored. The Mammalian Genotyping Service typed 391 microsatellite markers, spaced roughly 9 cM. Variance component linkage analysis was performed in SOLAR using ethnic-specific marker allele frequencies and multipoint IBDs calculated in MERLIN. We detected a quantitative trait locus influencing fasting insulin in female subjects (logarithm of odds [LOD] = 3.4) on chromosome 2 at 95 cM (between GATA69E12 and GATA71G04) but not in male subjects (LOD = 0.0, P for interaction = 0.007). This sex-specific signal at 2p13.2 was detected in both European-American (LOD = 2.1) and African-American (LOD = 1.2) female subjects. Our findings overlap with several other linkage reports of insulin-related traits and demonstrate the importance of considering complex context-dependent interactions in the search for insulin-related genes.

Anorexia nervosa (AN) is a serious mental illness categorized by a failure to maintain a minimally normal weight, a fear of gaining weight or becoming fat, and preoccupations about body shape or weight. AN is associated with significant morbidity and a mortality rate as high as that seen in any psychiatric illness. Biological factors, including genetic predisposition, appear to play a role in the development of AN. Treatment is challenging both because interventions with clear empirical support have not been identified and because individuals affected by AN are typically reluctant to undergo weight restoration. Preliminary studies suggest that family-based treatment may be useful for younger patients with AN. Treatment development for adults with AN and pursuit of neurobiological correlates of AN remain high-priority research areas. Expected final online publication date for the Annual Review of Medicine Volume 61 is January 07, 2010. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Empirically testable assumptions relate 3 psychophysical primitives: presentations of pairs of physical intensities (e.g., pure tones of the same frequency and phase to the 2 ears or 2 successive tones to both ears); a respondent's ordering of such signal pairs by perceived intensity (e.g., loudness); and judgments about 2 pairs of stimuli being related as some proportion (numerical factor, as in magnitude production). Explicit behavioral assumptions lead to 2 families of psychophysical functions, one corresponding to unbiased joint presentations and the other to biased ones. Under an invariance assumption, the psychophysical functions in the unbiased case are approximate power functions, and those in the biased case are exact power functions. A number of testable predictions are made. The mathematics involved draws from publications in utility theory and mathematics but with a reinterpretation of the primitives.

OBJECTIVES: To study health inequalities in persons with intellectual disabilities, representative and unbiased samples are needed. Little is known about sample recruitment in this vulnerable group. This study aimed to determine differences in ethical procedures and sample recruitment in a multicenter research on health of persons with intellectual disabilities. Study questions regarded the practical sampling procedure, how ethical consent was obtained in each country, and which person gave informed consent for each study participant. STUDY DESIGN AND SETTING: Exploratory, as part of a multicenter study, in 14 European countries. After developing identical guidelines for all countries, partners collected data on health indicators by orally interviewing 1,269 persons with intellectual disabilities. Subsequently, semistructured interviews were carried out with partners and researchers. RESULTS: Identification of sufficient study participants proved feasible. Sampling frames differed from nationally estimated proportions of persons with intellectual disabilities living with families or in residential settings. Sometimes, people with intellectual disabilities were hard to trace. Consent procedures and legal representation varied broadly. Nonresponse data proved unavailable. CONCLUSION: To build representative unbiased samples of vulnerable groups with limited academic capacities, international consensus on respectful consent procedures and tailored patient information is necessary.

Objective: To examine the relationship between selective nonresponse to a psychosocial questionnaire and mortality after acute myocardial infarction (AMI). Study Design and Setting: Two thousand six hundred and ninety AMI survivors after AMI hospitalization were recruited to complete a 30-day follow-up interview. Patients were classified into four groups (survey nonparticipation and complete, partial, and no item nonre- sponse) according to their degree of response to the Medical Outcomes Study (MOS) Social Support Survey (MOS-SSS). Cox proportional hazard models, adjusted for baseline sociodemographic, clinical, and psychosocial (i.e., social isolation) characteristics, were used to examine all-cause mortality, 3 years post-AMI, across the response levels. Results: 13.9%oftheeligiblepatientsrefusedfollow-upparticipation;MOS-SSSitemnonresponsewaspresentinupto14.7%ofparticipants and was more frequent among the elderly, socially disadvantaged, and those with higher clinical risk. A nonresponse mortality gradient existed, ranging from 8.9% (no item nonresponse) to 18.7% (complete item nonresponse) (P ! 0.001). After adjusting for baseline characteristics, complete item nonresponse remained significantly associated with mortality (hazard ratio: 1.33; 95% confidence interval: 1.02e1.73). Conclusions: Itemnonresponsetoasocialsupportquestionnaireisassociatedwithhighermortalitypost-AMI.Althoughexplanatoryfactors may include age and baseline clinical risk, additional psychosocial and/or unmeasured factors may account for the poorer prognosis.

The first part of this paper describes the use of the robust zR statistic to link test forms using the Rasch (or one-parameter logistic) model. The procedure is then extended to the two-parameter and three-parameter logistic and two-parameter partial credit (2PPC) models. A real set of data was used to illustrate the extension. The linking results illustrate the efficacy of the robust zR vis-à-vis some of the most commonly used processes such as the Stocking and Lord (1983) linking process.

Researchers often use as dependent variables quantities estimated from auxiliary data sets. Estimated dependent variable (EDV) models arise, for example, in studies where counties or states are the units of analysis and the dependent variable is an estimated mean, proportion, or regression coefficient. Scholars fitting EDV models have generally recognized that variation in the sampling variance of the observations on the dependent variable will induce heteroscedasticity. We show that the most common approach to this problem, weighted least squares, will usually lead to inefficient estimates and underestimated standard errors. In many cases, OLS with White's or Efron heteroscedastic consistent standard errors yields better results. We also suggest two simple alternative FGLS approaches that are more efficient and yield consistent standard error estimates. Finally, we apply the various alternative estimators to a replication of Cohen's (2004) cross-national study of presidential approval.

Lattice is an add-on package or library for the R statistical computing en- vironment. It provides an alternative set of user-level functions for producing graphical output. Compared to the base R graphical functions, the Lattice functions provide greater control over the specification of where graphical output appears on the page. In addition, Lattice graphics functions return graphical objects, which may be used to interactively edit the graphical out- put.

Most techniques for relating textual information rely on intellectually created links such as author-chosen keywords and titles, authority indexing terms, or bibliographic citations. Similarity of the semantic content of whole documents, rather than just titles, abstracts, or overlap of keywords, offers an attractive alternative. Latent semantic analysis provides an effective dimension reduction method for the purpose that reflects synonymy and the sense of arbitrary word combinations. However, latent semantic analysis correlations with human text-to-text similarity judgments are often empirically highest at approximately 300 dimensions. Thus, two- or three-dimensional visualizations are severely limited in what they can show, and the first and/or second automatically discovered principal component, or any three such for that matter, rarely capture all of the relations that might be of interest. It is our conjecture that linguistic meaning is intrinsically and irreducibly very high dimensional. Thus, some method to explore a high dimensional similarity space is needed. But the 2.7 x 10(7) projections and infinite rotations of, for example, a 300-dimensional pattern are impossible to examine. We suggest, however, that the use of a high dimensional dynamic viewer with an effective projection pursuit routine and user control, coupled with the exquisite abilities of the human visual system to extract information about objects and from moving patterns, can often succeed in discovering multiple revealing views that are missed by current computational algorithms. We show some examples of the use of latent semantic analysis to support such visualizations and offer views on future needs.

Inferences for the difference between two dependent intraclass correlation coefficients (ICCs) may arise in studies in which a sample of subjects are each assessed several times with a new device and a standard. The ICC estimates for the two devices may then be compared using a test of significance. However, a confidence interval for a difference between two ICCs is more informative since it combines point estimation and hypothesis testing into a single inference statement. We propose a procedure that uses confidence limits for a single ICC to recover variance estimates needed to set confidence limits for the difference. An advantage of this approach is that it provides a confidence interval that reflects the underlying sampling distribution. Simulation results show that this method performs very well in terms of overall coverage percentage and tail errors. Two data sets are used to illustrate this procedure.

Generalized Structured Component Analysis (GSCA) represents component- based structural equation modeling. Currently, GSCA is geared only for the analysis of quantitative data. In this paper, GSCA is extended to deal with qualitative data through data transformation. In particular, the optimal scaling approach is adopted for data transformation as it can be readily coupled with the GSCA estimation procedure. An alternating least-squares algorithm is developed that involves two phases for estimation of model and data parameters. Two empirical applications are presented to demonstrate the usefulness of the proposed method.

Bipolar disorder is a mood disorder characterized by impairing episodes of mania and depression. Twin studies have established that bipolar disorder is among the most heritable of medical disorders and efforts to identify specific susceptibility genes have intensified over the past two decades. The search for genes influencing bipolar disorder has been complicated by a paucity of animal models, limited understanding of pathogenesis, and the genetic and phenotypic complexity of the syndrome. Linkage studies have implicated several chromosomal regions as harboring relevant genes, but results have been inconsistent. It is now widely accepted that the genetic liability to bipolar disorder reflects the action of many genes of individually small effect, a scenario for which linkage studies are poorly suited. Thus, association studies, which are more powerful for the detection of modest effect loci, have become the focus of gene-finding research. A large number of candidate genes, including biological candidates derived from hypotheses about the pathogenesis of the disorder and positional candidates derived from linkage and cytogenetic studies, have been evaluated. Several of these genes have been associated with the disorder in independent studies (including BDNF, DAOA, DISC1, GRIK4, SLC6A4, and TPH2), but none has been established. The clinical heterogeneity of bipolar disorder and its phenotypic and genetic overlap with other disorders (especially schizophrenia, schizoaffective disorder, and major depressive disorder) have raised questions about the optimal phenotype definition for genetic studies. Nevertheless, genomewide association analysis, which has successfully identified susceptibility genes for a variety of complex disorders, has begun to implicate specific genes for bipolar disorder (DGKH, CACNA1C, ANK3). The polygenicity of the disorder means that very large samples will be needed to detect the modest effect loci that likely contribute to bipolar disorder. Detailed genetic dissection of the disorder may provide novel targets (both pharmacologic and psychosocial) for intervention.

OBJECTIVE: Logistic regression models are frequently used in cohort studies to determine the association between treatment and dichotomous outcomes in the presence of confounding variables. In a logistic regression model, the association between exposure and outcome is measured using the odds ratio (OR). The OR can be difficult to interpret and only approximates the relative risk (RR) in certain restrictive settings. Several authors have suggested that for dichotomous outcomes, RRs, RR reductions, absolute risk reductions, and the number needed to treat (NNT) are more clinically meaningful measures of treatment effect. STUDY DESIGN AND SETTING: We describe a method for deriving clinically meaningful measures of treatment effect from a logistic regression model. This method involves determining the probability of the outcome if each subject in the cohort was treated and if each subject was untreated. These probabilities are then averaged across the study cohort to determine the average probability of the outcome in the population if all subjects were treated and if they were untreated. RESULTS: Risk differences, RRs, and NNTs were derived using a logistic regression model. CONCLUSIONS: Clinically meaningful measures of effect can be derived from a logistic regression model in a cohort study. These methods can also be used in randomized controlled trials when logistic regression is used to adjust for possible imbalance in prognostically important baseline covariates.

BACKGROUND: Delineating the genetic basis of body composition is important to agriculture and medicine. In addition, the incorporation of gene-gene interactions in the statistical model provides further insight into the genetic factors that underlie body composition traits. We used Bayesian model selection to comprehensively map main, epistatic and sex-specific QTL in an F2 reciprocal intercross between two chicken lines divergently selected for high or low growth rate. RESULTS: We identified 17 QTL with main effects across 13 chromosomes and several sex-specific and sex-antagonistic QTL for breast meat yield, thigh + drumstick yield and abdominal fatness. Different sets of QTL were found for both breast muscles [Pectoralis (P) major and P. minor], which suggests that they could be controlled by different regulatory mechanisms. Significant interactions of QTL by sex allowed detection of sex-specific and sex-antagonistic QTL for body composition and abdominal fat. We found several female-specific P. major QTL and sex-antagonistic P. minor and abdominal fatness QTL. Also, several QTL on different chromosomes interact with each other to affect body composition and abdominal fatness. CONCLUSIONS: The detection of main effects, epistasis and sex-dimorphic QTL suggest complex genetic regulation of somatic growth. An understanding of such regulatory mechanisms is key to mapping specific genes that underlie QTL controlling somatic growth in an avian model.

This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of “construct validity” as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.

OBJECTIVE: To assess health-related quality of life (HRQOL) in methadone maintenance treatment (MMT) patients with untreated chronic HCV infection and to determine the clinical factors that predict HRQOL. METHOD: HRQOL was measured in 100 MMT patients entering an HCV treatment trial. Subjects were mostly male (61%) and white (81%) with a mean age of 43 (+/-10). 57% had a current non-substance use psychiatric disorder. 55% had a current (past 12 months) substance use disorder, including 44% with current opioid or cocaine abuse/dependence. HRQOL in our sample was compared to published reports for the general population as well as for non-MMT HCV patients. To assess predictors of SF-36 HRQOL, hierarchical multiple regression techniques were used to assess model improvement with four blocks of baseline predictors: Demographics, Medical Severity, Addiction Severity, and Depression Severity. RESULTS: HRQOL scores were significantly lower than scores for the general population and were also lower than scores reported for untreated HCV patients not in MMT. Regression analysis demonstrated a consistent pattern whereby Depression Severity increased predictive accuracy for HRQOL measures over simpler models. Beck Depression Inventory scores significantly predicted quality of life across both the mental and physical composite scores and all eight sub-scales of the SF-36. CONCLUSIONS: Untreated HCV patients in MMT had lower HRQOL than HCV patients not in MMT. Depression Severity was associated with significantly lower quality of life measures, suggesting that psychiatric evaluation and intervention prior to the start of HCV treatment may improve overall quality of life and could influence HCV treatment outcomes in MMT patients.

This trial of cognitive-behavioural therapy (CBT) based amphetamine abstinence program (n=507) focused on refusal self-efficacy, improved coping, improved problem solving and planning for relapse prevention. Measures included the Severity of Dependence Scale (SDS), the General Health Questionnaire-28 (GHQ-28) and Amphetamine Refusal Self-Efficacy. Psychiatric case identification (caseness) across the four GHQ-28 sub-scales was compared with Australian normative data. Almost 90% were amphetamine-dependent (SDS 8.15+/-3.17). Pre-treatment, all GHQ-28 sub-scale measures were below reported Australian population values. Caseness was substantially higher than Australian normative values Somatic Symptoms (52.3%), Anxiety (68%), Social Dysfunction (46.5%) and Depression (33.7%). One hundred and sixty-eight subjects (33%) completed and reported program abstinence. Program completers reported improvement across all GHQ-28 sub-scales Somatic Symptoms (p<0.001), Anxiety (p<0.001), Social Dysfunction (p<0.001) and Depression (p<0.001). They also reported improvement in amphetamine refusal self-efficacy (p<0.001). Improvement remained significant following intention-to-treat analyses, imputing baseline data for subjects that withdrew from the program. The GHQ-28 sub-scales, Amphetamine Refusal Self-Efficacy Questionnaire and the SDS successfully predicted treatment compliance through a discriminant analysis function (p<.001).

The impact of cigarette smoking on stroke incidence was assessed in the Framingham Heart Study cohort of 4255 men and women who were aged 36 to 68 years and free of stroke and transient ischemic attacks. During 26 years of follow-up, 459 strokes occurred. Regardless of smoking status and in each sex, hypertensive subjects had twice the incidence of stroke. Using the Cox proportional hazard regression method, smoking was significantly related to stroke after age and hypertension were taken into account. Even after pertinent cardiovascular disease risk factors were added to the Cox model, cigarette smoking continued to make a significant independent contribution to the risk of stroke generally and brain infarction specifically. The risk of stroke increased as the number of cigarettes smoked increased. The relative risk of stroke in heavy smokers (greater than 40 cigarettes per day) was twice that of light smokers (fewer than ten cigarettes per day). Lapsed smokers developed stroke at the same level as nonsmokers soon after stopping. Stroke risk decreased significantly by two years and was at the level of nonsmokers by five years after cessation of cigarette smoking.

This paper comments on an article by Schmidt and Hunter [Intelligence 27 (1999) 183.], who argue that the correction for attenuation should be routinely used in theory testing. It is maintained that Schmidt and Hunter's arguments are based on mistaken assumptions. We discuss our critique of Schmidt and Hunter in terms of two arguments against a routine use of the correction for attenuation within the classical test theory framework: (1) corrected correlations do not, as Schmidt and Hunter claim, provide correlations between constructs, and (2) corrections for measurement error should be made using modern test theory models instead of the classical model. The arguments that Schmidt and Hunter advance in favor of the correction for attenuation can be traced to an implicit identification of true scores with construct scores. First, we show that this identification confounds issues of validity and issues of reliability. Second, it is pointed out that equating true scores with construct scores is logically inconsistent with the classical test theory model itself. Third, it is argued that the classical model is not suited for detecting the dimensionality of test scores, which severely limits the interpretation of the corrected correlation coefficients. It is concluded that most measurement problems in psychology concern issues of validity, and that the correction for attenuation within classical test theory does not help in solving them.

The ability of positive and negative facial signals to influence attention orienting is crucial to social functioning. Given the dramatic developmental change in neural architecture supporting social function, positive and negative facial cues may influence attention orienting differently in relatively young or old individuals. However, virtually no research examines such age-related differences in the neural circuitry supporting attention orienting to emotional faces. We examined age-related correlations in attention-orienting biases to positive and negative face emotions in a healthy sample (N=37; 9-40 years old) using functional magnetic resonance imaging and a dot-probe task. The dot-probe task in an fMRI setting yields both behavioral and neural indices of attention biases towards or away from an emotional cue (happy or angry face). In the full sample, angry-face attention bias scores did not correlate with age, and age did not correlate with brain activation to angry faces. However, age did positively correlate with attention bias towards happy faces; age also negatively correlated with left cuneus and left caudate activation to a happy bias fMRI contrast. Secondary analyses suggested age-related changes in attention bias to happy faces. The tendency in younger children to direct attention away from happy faces (relative to neutral faces) was diminished in the older age groups, in tandem with increasing neural deactivation. Implications for future work on developmental changes in attention-emotion processing are discussed.

OBJECTIVE: Correspondence analysis (CA) is a multivariate graphical technique designed to explore the relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with the goal of examining associations among these variables. Nevertheless, CA appears to be an underused technique in epidemiology. The objective of this article is to present the utility of CA in an epidemiological context. STUDY DESIGN AND SETTING: The theory and interpretation of CA in the case of two and more than two variables are illustrated through two examples. RESULTS: The outcome from CA is a graphical display of the rows and columns of a contingency table that is designed to permit visualization of the salient relationships among the variable responses in a low-dimensional space. Such a representation reveals a more global picture of the relationships among row-column pairs, which would otherwise not be detected through a pairwise analysis. CONCLUSION: When the study variables of interest are categorical, CA is an appropriate technique to explore the relationships among variable response categories and can play a complementary role in analyzing epidemiological data.

BACKGROUND: In June 2004, the National Cancer Institute and the Drug Information Association co-sponsored the conference, "Improving the Measurement of Health Outcomes through the Applications of Item Response Theory (IRT) Modeling: Exploration of Item Banks and Computer-Adaptive Assessment." A component of the conference was presentation of a psychometric and content analysis of a secondary dataset. OBJECTIVES: A thorough psychometric and content analysis was conducted of two primary domains within a cancer health-related quality of life (HRQOL) dataset. RESEARCH DESIGN: HRQOL scales were evaluated using factor analysis for categorical data, IRT modeling, and differential item functioning analyses. In addition, computerized adaptive administration of HRQOL item banks was simulated, and various IRT models were applied and compared. SUBJECTS: The original data were collected as part of the NCI-funded Quality of Life Evaluation in Oncology (Q-Score) Project. A total of 1,714 patients with cancer or HIV/AIDS were recruited from 5 clinical sites. MEASURES: Items from 4 HRQOL instruments were evaluated: Cancer Rehabilitation Evaluation System-Short Form, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, Functional Assessment of Cancer Therapy and Medical Outcomes Study Short-Form Health Survey. RESULTS AND CONCLUSIONS: Four lessons learned from the project are discussed: the importance of good developmental item banks, the ambiguity of model fit results, the limits of our knowledge regarding the practical implications of model misfit, and the importance in the measurement of HRQOL of construct definition. With respect to these lessons, areas for future research are suggested. The feasibility of developing item banks for broad definitions of health is discussed.

BACKGROUND: Impulsivity is a multifaceted construct that has recently been recognized as a factor contributing to enhanced vulnerability to drug abuse. OBJECTIVES: In the present review, we focus on two facets of impulsivity (and tasks that measure them): (1) impulsive choice (delay discounting task) and (2) inhibitory failure (go/no-go, stop signal reaction time, and five-choice serial reaction time tasks). We also describe how performance on each of these tasks is associated with drug-related behavior during phases of drug abuse that capture the essential features of addiction (acquisition, escalation, and reinstatement of drug-seeking after drug access has terminated). Three hypotheses (H) regarding the relationship between impulsivity and drug abuse are discussed: (1) increased levels of impulsivity lead to drug abuse (H1), (2) drugs of abuse increase impulsivity (H2), and (3) impulsivity and drug abuse are associated through a common third factor (H3). CONCLUSION: Impulsivity expressed as impulsive choice or inhibitory failure plays a role in several key transition phases of drug abuse. There is evidence to support all three nonexclusive hypotheses. Increased levels of impulsivity lead to acquisition of drug abuse (H1) and subsequent escalation or dysregulation of drug intake. Drugs of abuse may increase impulsivity (H2), which is an additional contributor to escalation/dysregulation. Abstinence, relapse, and treatment may be influenced by both H1 and H2. In addition, there is a relationship between impulsivity and other drug abuse vulnerability factors, such as sex, hormonal status, reactivity to nondrug rewards, and early environmental experiences that may impact drug intake during all phases of addiction (H3). Relating drug abuse and impulsivity in phases of addiction via these three hypotheses provides a heuristic model from which future experimental questions can be addressed.

Relationships between genomic data and functional brain images are of great interest but require new analysis ap- proaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced inter- esting findings.

The focus of this study was the incidence of different kinds of missing-data problems in personality research and the handling of these problems. Missing-data problems were reported in approximately half of more than 800 articles published in three leading personality journals. In these articles, unit nonresponse, attrition, and planned missingness were distinguished but missing item scores in trait measurement were reported most frequently. Listwise deletion was the most frequently used method for handling all missing-data problems. Listwise deletion is known to reduce the accuracy of parameter estimates and the power of statistical tests and often to produce biased statistical analysis results. This study proposes a simple alternative method for handling missing item scores, known as two-way imputation, which leaves the sample size intact and has been shown to produce almost unbiased results based on multi-item questionnaire data.

Population stratification can be a serious obstacle in the analysis of genomewide association studies. We propose a method for evaluating the significance of association scores in whole-genome cohorts with stratification. Our approach is a randomization test akin to a standard permutation test. It conditions on the genotype matrix and thus takes into account not only the population structure but also the complex linkage disequilibrium structure of the genome. As we show in simulation experiments, our method achieves higher power and significantly better control over false-positive rates than do existing methods. In addition, it can be easily applied to whole-genome association studies.

BACKGROUND: The usual method of assessing the variability of a measure such as the ankle brachial index (ABI) as a function of different observer groups is to obtain repeated measurements. Because the number of possible observer-subject combinations is impractically large, only a few small studies on inter- and intraobserver variability of ABI measures have been carried out to date. The present study proposes a new and efficient study design. This paper describes the study methodology. METHODS: Using a partially balanced incomplete block design, six angiologists, six primary-care physicians and six trained medical office assistants performed two ABI measurements each on six individuals from a group of 36 unselected subjects aged 65-70 years. Each test subject is measured by one observer from each of the three observer groups, and each observer measures exactly six of the 36 subjects in the group. Each possible combination of two observers occurs exactly once per patient and is not repeated on a second subject. The study involved four groups of 36 subjects (144), plus standbys. RESULTS: The 192 volunteers present at the study day were similar in terms of demographic characteristics and vascular risk factors: mean age 68.6 +/- 1.7; mean BMI 29.1 +/- 4.6; mean waist-hip ratio 0.92 +/- 0.09; active smokers 12%; hypertension 60.9%; hypercholesterolemia 53.4%; diabetic 17.2%. A complete set of ABI measurements (three observers performing two Doppler measurements each) was obtained from 108 subjects. From all other subjects at least one ABI measurement was obtained. The mean ABI was 1.08 (+/- 0.13), 15 (7.9%) volunteers had an ABI < 0.9, and none had an ABI > 1.4, i.e. a ratio that may be associated with increased stiffening of the arterial walls. CONCLUSION: This is the first large-scale study investigating the components of variability and thus reliability in ABI measurements. The advantage of the new study design introduced here is that only one sixth of the number of theoretically possible measurements is required to obtain information about measurement errors. Bland-Altman plots show that there are only small differences and no systematic bias between the observers from three occupational groups with different training backgrounds.

Gray (1987) proposed two systems that underlie much of our behaviour and personality. One system relates to avoidance or withdrawal behaviour, called the Behavioural Inhibition System (BIS), whereas the other system relates to approach behaviour, called the Behavioural Approach System (BAS). In two samples, it was investigated whether individual differences in surface of personality as described by the Big Five can be explained by BIS/BAS. Neuroticism and Extraversion could be explained well by BIS/BAS, but also for Agreeableness and Conscientiousness consistent findings were obtained.

The purpose of cancer genome sequencing studies is to determine the nature and types of alterations present in a typical cancer and to discover genes mutated at high frequencies. In this article we discuss statistical methods for the analysis of data generated in these studies. We place special emphasis on a two-stage study design introduced by Sjoblom et al.[1]. In this context, we describe statistical methods for constructing scores that can be used to prioritize candidate genes for further investigation and to assess the statistical signicance of the candidates thus identfied.

Les tests omnibus plusieurs degrés de libertés ne fournissent que des réponses vagues alors que la plupart des hypothèses que nous dérivons de nos modèles théoriques/ont des prédictions relativement précises Pour répondre ce niveau de précision nous suggérons de tester des contrastes spécifiques plutôt que effectuer des tests omnibus Deux conditions se doivent être satisfaites avant que on puisse affirmer un contraste donné est une description parci monieuse des moyennes observées le contraste lui-même doit expliquer une partie significative de la variance et si on contrôle statistiquement les effets de ce contraste la variance intergroupe résiduelle doit être non significa tive aide exemples concrets article présente les analyses permettant de tester ces deux conditions avec différents plans expérimentaux Mots clés contraste test omnibus variance résiduelle degré de liberte

We address two long-standing survey research problems: measuring complicated concepts, such as political freedom and efficacy, that researchers define best with reference to examples; and what to do when respondents interpret identical questions in different ways. Scholars have long addressed these problems with approaches to reduce incomparability, such as writing more concrete questions-with uneven success. Our alternative is to measure directly response category incomparability and to correct for it. We measure incomparability via respondents' assessments, on the same scale as the self-assessments to be corrected, of hypothetical individuals described in short vignettes. Because the actual (but not necessarily reported) levels of the vignettes are invariant over respondents, variability in vignette answers reveals incomparability. Our corrections require either simple recodes or a statistical model designed to save survey administration costs. With analysis, simulations, and cross-national surveys, we show how response incomparability can drastically mislead survey researchers and how our approach can alleviate this problem.

In the last few years, numerous methods have been proposed for microarray-based class prediction. Although many of them have been designed especially for the case n << p (much more variables than observations), preliminary variable selection is almost always necessary when the number of genes reaches several tens of thousands, as usual in recent data sets. In the two-class setting, the Wilcoxon rank sum test statistic is, with the t-statistic, one of the standard approaches for variable selection. It is well known that the variable selection step must be seen as a part of classifier construction and, as such, be performed based on training data only. When classifier accuracy is evaluated via cross-validation or Monte-Carlo cross-validation, it means that we have to perform p Wilcoxon or t-tests for each iteration, which becomes a daunting task for increasing p. As a consequence, many authors often perform variable selection only once using all the available data, which can induce a dramatic underestimation of error rate and thus lead to misleadingly reporting predictive power. We propose a very fast implementation of variable selection based on the Wilcoxon test for use in cross-validation and Monte Carlo cross-validation (also known as random splitting into learning and test sets). This implementation is based on a simple mathematical formula using only the ranks calculated from the original data set. Availability: Our method is implemented in the freely available R package WilcoxCV which can be downloaded from the Comprehensive R Archive Network at http://cran.r-project.org/src/contrib/Descriptions/WilcoxCV.html.

A methodological study was conducted to examine the effect of extending a frequently used simple multiattribute health-status classification system by adding a cognitive dimension. The EQ-5D questionnaire is a generic instrument to value health, developed by the EuroQol Group. The EQ-5D defines health according to five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. We defined 18 different health states according to the standard EQ-5D classification. A second set of health states was constructed similar to the first, except for the addition of a cognitive dimension (EQ-5D+C). Valuations of both sets of health states were statistically analyzed to detect the effect of the additional dimension. The cognitive dimension generated systematically different values compared with the standard EQ-5D version, whereas the content validity improved. Both systems evoked equally reliable values. Analyses showed that a simple additive model to predict summary values for health states was not optimal for both systems. Although there is a current lack of consensus regarding the domains that are selected to represent health status, this study has shown the importance of considering the inclusion of a cognitive domain.

Major challenges in the evaluation of the "end results" of health services include ensuring that concepts are correctly defined and measured, that the validity of measures used in different applications and populations is well documented, and that observed effects can be clearly interpreted. Health status is the most widely interpretable concept to apply in the context of health services. Quality of life connotes inclusion of the environment outside the context of the person and of health care and may or may not be health related, depending on the evaluation context and the impact of disease and treatment. All concepts and constructs must be defined in reference to their theoretical origin and to a model of relationships among different concepts. Modern test theory offers the potential for individualized, comparable assessments and for the careful examination and application of different measurement models. Selection and critique of measures should be based on the intended application and accumulated evidence for that application. Thus, there are no valid instruments per se. Validity in use, including responsiveness, interpretation of effects, and generalizability to diverse populations, is the most important measurement characteristic for treatment effectiveness. An evaluation of the validity of preference-based measures is particularly important for the interpretation and comparability of outcomes in cost-effectiveness evaluations. The successful translation of research into policy and practice is limited by the extent to which these critical issues are addressed in actual treatment evaluations.

Although the importance of selecting cases and controls from the same population has been recognized for decades, the recent advent of genome-wide association studies has heightened awareness of this issue. Because these studies typically deal with large samples, small differences in allele frequencies between cases and controls can easily reach statistical significance. When, unbeknownst to a researcher, cases and controls have different substructures, the number of false-positive findings is inflated. There have been three recent developments of purely statistical approaches to assessing the ancestral comparability of case and control samples: genomic control, structured association, and multivariate reduction analyses. The widespread use of high-throughput technology has allowed the quick and accurate genotyping of the large number of markers required by these methods. Group 13 dealt with four population stratification issues: single-nucleotide polymorphism marker selection, association testing, nonstandard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both data sets of Genetic Analysis Workshop 16. Furthermore, ignoring this structure created P-value inflation for a variety of phenotypes. Principal-components analysis (or multidimensional scaling) can control inflation as covariates in a logistic regression. One can weigh for local ancestry estimation and allow the use of related individuals. Problems arise in the presence of extremely high association or unusually strong linkage disequilibrium (e.g., in chromosomal inversions). Our group also reported a method for performing an association test controlling for substructure, when genome-wide markers are not available, to explicitly compute stratification.

MOTIVATION: Several tools that facilitate the interpretation of transcriptional profiles using gene annotation data are available but most of them combine a particular statistical analysis strategy with functional information. goCluster extends this concept by providing a modular framework that facilitates integration of statistical and functional microarray data analysis with data interpretation. RESULTS: goCluster enables scientists to employ annotation information, clustering algorithms and visualization tools in their array data analysis and interpretation strategy. The package provides four clustering algorithms and GeneOntology terms as prototype annotation data. The functional analysis is based on the hypergeometric distribution whereby the Bonferroni correction or the false discovery rate can be used to correct for multiple testing. The approach implemented in goCluster was successfully applied to interpret the results of complex mammalian and yeast expression data obtained with high density oligonucleotide microarrays (GeneChips). AVAILABILITY: goCluster is available via the BioConductor portal at www.bioconductor.org. The software package, detailed documentation, user- and developer guides as well as other background information are also accessible via a web portal at http://www.bioz.unibas.ch/gocluster CONTACT: michael.primig@unibas.ch

That the human brain is the organ of the mind is not in dispute, but we know remarkably little about the brain mechanisms underlying the mind. What are the functional structures and computational processes of the human brain that subserve cognition, emotion, and be- havior? Given the complexity of the human brain, progress in understanding the functional organization and struc- ture of the human brain depends on sophisticated theo- retical specifications of the psychological representations and processes that differentiate two or more comparison conditions. Psychological scientists, therefore, are well positioned to lead the search for brain mechanisms un- derlying psychological processes. Doing so constitutes an expansion of the purview of psychological science beyond a science of behavior, and beyond a science of the mind, to include a science of the brain. Such an expansion of the mission of psychological science has implications for the infrastructure and training needs of the discipline.

ABSTRACT : Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphisms (SNPs) for association to a trait, treating each marker equally and ignoring prior evidence of association to specific regions. Typically, promising regions are selected for further investigation based on p-values obtained from simple tests of association. However, loci that exert only a weak, low-penetrant role on the trait, producing modest evidence of association, are not detectable in the context of a GWAS. Implementing prior knowledge of association in GWAS could increase power, help distinguish between false and true positives, and identify better sets of SNPs for follow-up studies.Here we performed a GWAS on rheumatoid arthritis (RA) patients and controls (Problem 1, Genetic Analysis Workshop 16). In order to include prior information in the analysis, we applied four methods that distinctively deal with markers in candidate genes in the context of GWAS. SNPs were divided into a random and a candidate subset, then we applied empirical correction by permutation, false-discovery rate, false-positive report probability, and posterior odds of association using different prior probabilities. We repeated the same analyses on two different sets of candidate markers defined on the basis of previously reported association to RA following two different approaches. The four methods showed similar relative behavior when applied to the two sets, with the proportion of candidate SNPs ranked among the top 2,000 varying from 0 to 100%. The use of different prior probabilities changed the stringency of the methods, but not their relative performance.

Gene-environment interaction was studied in a sample of young (mean age 26 years, N = 385) and older (mean age 49 years, N = 370) adult males and females. Full scale IQ scores (FSIQ) were analyzed using biometric models in which additive genetic (A), common environmental (C), and unique environmental (E) effects were allowed to depend on environmental measures. Moderators under study were parental and partner educational level, as well as urbanization level and mean real estate price of the participants' residential area. Mean effects were observed for parental education, partner education and urbanization level. On average, FSIQ scores were roughly 5 points higher in participants with highly educated parents, compared to participants whose parents were less well educated. In older participants, IQ scores were about 2 points higher when their partners were highly educated. In younger males, higher urbanization levels were associated with slightly higher FSIQ scores. Our analyses also showed increased common environmental variation in older males whose parents were more highly educated, and increased unique environmental effects in older males living in more affluent areas. Contrary to studies in children, however, the variance attributable to additive genetic effects was stable across all levels of the moderators under study. Most results were replicated for VIQ and PIQ.

An important problem in principal component analysis (PCA) is the estimation of the correct number of components to retain. PCA is most often used to reduce a set of observed variables to a new set of variables of lower dimensionality. The choice of this dimensionality is a crucial step for the interpretation of results or subsequent analyses, because it could lead to a loss of information (underestimation) or the introduction of random noise (overestimation). New techniques are proposed to evaluate the dimensionality in PCA. They are based on similarity measurements, singular value decomposition and permutation procedures. A simulation study is conducted to evaluate the relative merits of the proposed approaches. Results showed that one method based on the RV coefficient is very accurate and seems to be more efficient than other existing approaches.

Jensen has posited a research method to investigate group differences in cognitive tests. This method consists of first extracting a general intelligence factor by means of exploratory factor analysis. Secondly, similarity of factor loadings across groups is evaluated in an attempt to ensure that the same constructs are measured. Finally, the correlation is computed between the loadings of the tests on the general intelligence factor and the mean differences between groups on the tests. This part is referred to as a test of “Spearman's Hypothesis”, which essentially states that differences in g account for the main part of differences in observed scores. Based on the correlation, inferences are made with respect to group differences in general intelligence. The validity of these inferences is investigated and compared to the validity of inferences based on multi-group confirmatory factor analysis. For this comparison, population covariance matrices are constructed which incorporate violations of the central assumption underlying Jensen's method concerning the existence of g and/or violations of Spearman's Hypothesis. It is demonstrated that Jensen's method is quite insensitive to the violations. This lack of specificity is observed consistently for all types of violations introduced in the present study. Multi-group confirmatory factor analysis emerges as clearly superior to Jensen's method.

Variance-based interrater agreement indices in the rWG family are often interpreted using rules-of- thumb derived for reliabilities (e.g., ≥ .70 = acceptable). Monte Carlo results suggest that far more stringent standards are needed, especially for maximum-variance rWG, as values > .70 can routinely be obtained from totally random ratings.

The objective of this article is to help clinicians interpret trial-based quality of life (QOL) changes over time. We address a series of questions and provide guidelines that are fundamental to assessing and interpreting change. The issues addressed are as follows: (1) What are the characteristics of the population for whom changes in QOL are reported? (2) Is the QOL questionnaire reliable, valid, and responsive to change? (3) Are the timing and frequency of assessments adequate? (4) Is the study adequately powered? (5) How are multiple QOL outcomes addressed in analyses? (6) How are multiple time points handled? (7) Can alternative explanations account for the observed change or lack of observed change (eg, handling of missing data, survival differences, and changes in patient's QOL perspective over time)? and (8) How is statistical significance translated into meaningful change? These guidelines will support clinicians in reviewing the clinical trial literature, which in turn can help them use the data in the treatment decision process.

OBJECTIVES: To assess aspects of the internal validity of recently published cluster randomised trials and explore the reporting of information useful in assessing the external validity of these trials. DESIGN: Review of 34 cluster randomised trials in primary care published in 2004 and 2005 in seven journals (British Medical Journal, British Journal of General Practice, Family Practice, Preventive Medicine, Annals of Internal Medicine, Journal of General Internal Medicine, Pediatrics). DATA SOURCES: National Library of Medicine (Medline) via PubMed. DATA EXTRACTION: To assess aspects of internal validity we extracted data on appropriateness of sample size calculations and analyses, methods of identifying and recruiting individual participants, and blinding. To explore reporting of information useful in assessing external validity we extracted data on cluster eligibility, cluster inclusion and retention, cluster generalisability, and the feasibility and acceptability of the intervention to health providers in clusters. RESULTS: 21 (62%) trials accounted for clustering in sample size calculations and 30 (88%) in the analysis; about a quarter were potentially biased because of procedures surrounding recruitment and identification of patients; individual participants were blind to allocation status in 19 (56%) and outcome assessors were blind in 15 (44%). In almost half the reports, information relating to generalisability of clusters was poorly reported, and in two fifths there was no information about the feasibility and acceptability of the intervention. CONCLUSIONS: Cluster randomised trials are essential for evaluating certain types of interventions. Issues affecting their internal validity, such as appropriate sample size calculations and analysis, have been widely disseminated and are now better addressed by researchers. Blinding of those identifying and recruiting patients to allocation status is recommended but is not always carried out. There may be fewer barriers to internal validity in trials in which individual participants are not recruited. External validity seems poorly addressed in many trials, yet is arguably as important as internal validity in judging quality as a basis for healthcare intervention.

The concept of "mild cognitive impairment" (MCI) refers to alterations in cognition in the transition between normal aging and dementia. However, from a neuropsychological point of view the conventional diagnostic criteria appear not sufficiently valid. In particular, it is still difficult to differentiate between subjects with MCI and subjects with depression plus cognitive deficits on the basis of their neuropsychological profiles. The aim of this study is to compare cognitive deficit patterns of subjects with MCI and with depression. 24 subjects with MCI, 50 subjects with depression (DEP) and 20 healthy control subjects were included (age: 55-74years). The neuropsychological assessment consisted of standardized tests to assess attention, memory, and executive functions. Compared to healthy controls both subject groups showed significantly lower performance in all cognitive domains. However, we did not find significant differences in cognitive performance between MCI and DEP subjects, neither at baseline nor at follow-up. In addition, preliminary results of follow-up assessments after 2 (DEP) and 6months (MCI), respectively, revealed no significant changes in cognition in subjects with depression, regardless of whether depressive symptoms had improved. Subjects with MCI also showed no changes in cognition at follow-up. The comparable neuropsychological patterns identified in the two subject groups may be understood as a consequence of similar alterations in cognitive systems, supporting the idea of a final common pathway disorder. Thus, the cognitive deficits present in a subgroup of subjects with depression may possibly better be understood in the context of MCI.

Risk for cocaine dependence (CD) is genetically influenced. We recruited a sample of small nuclear families (528 full and 155 half sibpairs) with at least one subject affected with CD. The sample was classified via Bayesian clustering as 45.5% European American (EA) and 54.5% African American (AA). Assessment, via the Semi-Structured Assessment for Drug Dependence and Alcoholism, allowed for detailed evaluation of substance dependence-related traits. To define subgroups with increased genetic homogeneity, consistent with our a priori analytic plan, we used cluster analytic methods to identify six cocaine-related symptom clusters; membership was shown to be significantly heritable. We then completed a genomewide linkage scan (409 markers) for the CD diagnosis, cocaine-induced paranoia (CIP; an outcome that occurs in some cocaine users) and the clusters (three of which contained >80% of the CD subjects). We observed a "suggestive" linkage signal on chromosome 10 for the trait of CD in the full sample; and two "suggestive" linkage signals at different locations on chromosome 3, in the EA part of the sample. We observed a genomewide-significant lod score of 3.65 for the trait of CIP on chromosome 9, in the AA part of the sample only. Our strongest results were observed for the cluster membership traits, including a lod score of 4.66 for membership in the "Heavy Use, Cocaine Predominant" cluster on chromosome 12 (in EAs only) and a lod score of 3.35 for membership in the "Moderate Cocaine and Opioid Abuse" cluster on chromosome 18. These results provide a basis for the identification of specific genes contributing to risk for these traits.

In order to disentangle genetic and environmental contributions to cortical anomalies in children with autism, we investigated cortical folding patterns in a cohort of 14 monozygotic (MZ) twin pairs who displayed a range of phenotypic discordance for autism, and 14 typically developing community controls. Cortical folding was assessed with the gyrification index, which was calculated on high resolution anatomic MR images. We found that the cortical folding patterns across most lobar regions of the cerebral cortex was highly discordant within MZ twin pairs. In addition, children with autism and their co-twins exhibited increased cortical folding in the right parietal lobe, relative to age- and gender-matched typical developing children. Increased folding in the right parietal lobe was associated with more symptoms of autism for co-twins. Finally, the robust association between cortical folding and IQ observed in typical children was not observed in either children with autism or their co-twins. These findings, which contribute to our understanding of the limits of genetic liability in autism, suggest that anomalies in the structural integrity of the cortex in this PDD may disrupt the association between cortical folding and intelligence that has been reported in typical individuals, and may account, in part, for the deficits in visual spatial attention and in social cognition that have been reported in children with autism.

OBJECTIVES: To optimize, apply, and validate a scoring algorithm that provides a utility index from a cancer-specific quality of life questionnaire called the Utility-Based Questionnaire-Cancer (UBQ-C) using data sets from randomized trials in breast cancer. The index is designed to reflect the perspective of cancer patients in a specific clinical context so as to best inform clinical decisions. METHODS: We applied the UBQ-C scoring algorithm to trials of chemotherapy for advanced (n = 325) and early (n = 126) breast cancer. The algorithm converts UBQ-C subscales into a subset index, and combines it with a global health status item into an overall HRQL index, which is then converted to a utility index using a power transformation. The optimal subscale weights were determined by their correlations with the global scale in the relevant data set. The validity of the utility index was tested against other patient characteristics. RESULTS: Optimal weights (range 0-1) for the subset index in advanced (early) breast cancer were: physical function 0.20 (0.09); social/usual activities 0.23 (0.25); self-care 0.04 (0.01); and distresses 0.53 (0.64). Weights for the overall HRQL index were health status 0.66 (0.63) and subset index 0.34 (0.37). The utility index discriminated between breast cancer that was advanced rather than early (means 0.88 vs. 0.94, P < 0.0001) and was responsive to the toxic effects of chemotherapy in early breast cancer (mean change 0.07, P < 0.0001). CONCLUSIONS: The scoring algorithm for the UBQ-C utility index can be optimized in different clinical contexts to reflect the relative importance of different aspects of quality of life to the patients in a trial. It can be used to generate sensitive and responsive utility scores, and quality-adjusted life-years that can be used within a trial to compare the net benefit of treatments and inform clinical decision-making.

BACKGROUND: Postpartum anxiety and depression is a major public health concern because of its adverse effects on the cognitive and social development of the infant. Globally postpartum depression has been widely investigated but as anxiety is a more prominent feature of postpartum depression we assessed the prevalence of anxiety and depression and their associated factors in post partum women. METHODS: A quasi-experimental study investigating the impact of postpartum anxiety and depression on child growth and development was conducted in two peri-urban, multiethnic, communities of Karachi, a mega city of Pakistan. A house to house questionnaire based survey was done by trained field workers; 420 consenting pregnant women were identified and data for socio-demographic, home environment and family relationship variables was collected between 36 weeks of pregnancy and within 10 days of childbirth. Mother's levels of anxiety and depression were assessed after one month, two months, six months and twelve months of childbirth; this was two step process: initially an indigenous, validated screening instrument Aga Khan University Anxiety and Depression Scale was used and diagnostic confirmation was done through a psychologist's interview based on DSM IV criteria. Women found to be anxious and depressed at least once out of four assessments were considered for the computation of overall prevalence of postpartum anxiety and depression as well as its risk factors. However, point prevalence's of postpartum anxiety and depression were also reported at each assessment time. Two sixty seven women could be followed for one year. Data was analyzed using SPSS. Chi-square test, simple and multiple logistic regression were used to see the association of different factors. RESULTS: The overall prevalence of postpartum anxiety and depression was found to be 28.8 percent. Domestic violence, difficulty in breast feeding at birth and unplanned current pregnancy were found to be significantly associated with postpartum anxiety and depression. CONCLUSION: Domestic violence and not having the right to plan pregnancy are related to the patriarchal culture and lack of empowerment of women. The association with difficulties in breast feeding needs to be further explored in future studies.

Autism is a childhood neuropsychiatric disorder that, despite exhibiting high heritability, has largely eluded efforts to identify specific genetic variants underlying its etiology. We performed a two-stage genetic study in which genome-wide linkage and family-based association mapping was followed up by association and replication studies in an independent sample. We identified a common polymorphism in contactin-associated protein-like 2 (CNTNAP2), a member of the neurexin superfamily, that is significantly associated with autism susceptibility. Importantly, the genetic variant displays a parent-of-origin and gender effect recapitulating the inheritance of autism.

It has long been recognized that environmental stress plays a pivotal role in the pathogenesis of psychiatric disorders. The relationship is complex and the neurobiological mechanisms that mediate the contribution of stressful experiences to the manifestation of illness are not well understood. In considering this relationship, it is important to differentiate between the role of environmental stressors as vulnerability factors that predispose the individual to psychiatric illness and may be temporally distant from its clinical onset, and their role as direct precipitants of the illness. Furthermore, environmental stressors must be considered in the context of constitutional vulnerability factors, such as genetic predisposition, with which such stressors may interact. Genetic predisposition may influence not only vulnerability to illness but also the nature of the individual's response to stress and the likelihood of exposure to stressful events. In this paper, we focus on two areas that illustrate the complexity of the field and the important findings that have emerged-the role of early parental loss (EPL) in adult psychopathology, particularly major depression, and the relationship between recent significant life events and depressive episodes. We conclude with a preliminary conceptual framework for considering the relationship between genetic susceptibility and environmental stress in the pathogenesis of psychiatric illness.

Case-control studies are subject to the problem of population stratification, which can occur in ethnically mixed populations and can lead to significant associations being detected at loci that have nothing to do with disease. Here, we describe a way to measure and correct for stratification by genotyping a moderate number of unlinked genetic markers in the same set of cases and controls in which a candidate association was found. The average of association statistics across the markers directly measures stratification. By dividing the candidate association statistic by this average, a P-value can be obtained that corrects for stratification.

This paper reviews the published evidence on genetically driven variation in neurotransmitter function and brain circuits involved in emotion. Several studies point to a role of the serotonin transporter promoter polymorphism in amygdala activation during emotion perception. We also discuss other polymorphisms (e.g. the COMT val158met polymorphism, tryptophan hydroxylase-2 -703 G/T) and putative effects on affective processing in cortical and limbic regions. A different line of research concerns studies with genetic disorders. Although at a less fine-grained level, studies with individuals with aneuploidies of the X chromosome (Turner syndrome and Klinefelter syndrome), who display impairments in emotion processing, have resulted in new insights and hypotheses with regard to X chromosomal influences on brain systems supporting cognition and emotion. These have also implicated a key role for the amygdala. Integration of the emerging evidence, suggests that the study of polymorphisms using brain imaging can potentially elucidate biological pathways and mechanisms contributing to individual differences in brain circuits that may bias behavior and affect risk for psychiatric illness.

BACKGROUND: There is increasing interest in identifying adolescents with depression in primary care settings by paediatricians in India. This article studied the diagnostic accuracy, reliability and validity of Beck Depression Inventory (BDI) while used by paediatricians in a primary care setting in India. METHODS: 181 adolescents attending 3 schools were administered a back translated Tamil version of BDI by a paediatrician to evaluate its psychometric properties along with Children's Depression Rating Scale (CDRS-R) for convergent validity. Clinical diagnosis of depressive disorders, for reference standard, was based on ICD-10 interview by an independent psychiatrist who also administered the Impact of Event Scale (IES) for divergent validity. Appropriate analyses for validity and diagnostic accuracy both at the item and scale levels were conducted. RESULTS: A cut-off score of >or= 5 (Sn = 90.9%, Sp = 17.6 %) for screening and cut-off score of >or= 22 (Sn = 27.3%, Sp = 90%) for diagnostic utility is suggested. The 4 week test - retest reliability was good (r = 0.82). In addition to the adequate face and content validity, BDI has very good internal consistency (alpha = 0.96), high convergent validity with CDRS-R (r = 0.72; P = 0.001), and high discriminant validity with IES (r = 0.26; P = 0.23). There was a moderate concordance rate with the reference standard (54.5%) in identifying depression among the adolescents. Factor analysis replicated the 2-factor structure explaining 30.5 % of variance. CONCLUSION: The BDI proved to be a psychometrically sound measure for use by paediatricians in a primary care setting in India. The possibility of screening for depressive disorders through the use of BDI may be helpful in identifying probable cases of the disorder among adolescents.

BACKGROUND: The KINDL questionnaire is frequently used to evaluate quality of life (QOL) and the impacts of health conditions on children's everyday living. The objectives of this study were to assess the reproducibility and construct validity of the Serbian KINDL for QOL assessments in healthy children and adolescents. METHODS: Five hundred and sixty-four healthy children and adolescents completed the KINDL. Reproducibility was analyzed using the intraclass correlation coefficient (ICC). Confirmatory factor analysis (CFA) was performed to assess the structure of the KINDL construct validity. RESULTS: The intraclass correlation coefficients ranged from 0.03 to 0.84 for the subscales and total score. A second order CFA model as originally hypothesized was tested: items (24), primary factors (six subscales), and one secondary factor (QOL). The fit indexes derived from a CFA failed to yield appropriate fit between the data and the hypothesized model. CONCLUSION: Majority of the subscales and total KINDL possess appropriate reproducibility for group comparisons. However, a CFA failed to confirm the structure of the original measurement model, indicating that the Serbian version should be revised before wider use for QOL assessments in healthy children and adolescent.

A memory-based scaling model-ANCHOR-is proposed and tested. The perceived magnitude of the target stimulus is compared with a set of anchors in memory. Anchor selection is probabilistic and sensitive to similarity, base-level strength, and recency. The winning anchor provides a reference point near the target and thereby converts the global scaling problem into a local comparison. An explicit correction strategy determines the final response. Two incremental learning mechanisms update the locations and base-level activations of the anchors. This gives rise to sequential, context, transfer, practice, and other dynamic effects. The scale unfolds as an adaptive map. A hierarchy of models is tested on a battery of quantitative measures from 2 experiments in absolute identification and category rating.

Logistic regression analysis may well be used to develop a prognostic model for a dichotomous outcome. Especially when limited data are available, it is difficult to determine an appropriate selection of covariables for inclusion in such models. Also, predictions may be improved by applying some sort of shrinkage in the estimation of regression coefficients. In this study we compare the performance of several selection and shrinkage methods in small data sets of patients with acute myocardial infarction, where we aim to predict 30-day mortality. Selection methods included backward stepwise selection with significance levels alpha of 0.01, 0.05, 0. 157 (the AIC criterion) or 0.50, and the use of qualitative external information on the sign of regression coefficients in the model. Estimation methods included standard maximum likelihood, the use of a linear shrinkage factor, penalized maximum likelihood, the Lasso, or quantitative external information on univariable regression coefficients. We found that stepwise selection with a low alpha (for example, 0.05) led to a relatively poor model performance, when evaluated on independent data. Substantially better performance was obtained with full models with a limited number of important predictors, where regression coefficients were reduced with any of the shrinkage methods. Incorporation of external information for selection and estimation improved the stability and quality of the prognostic models. We therefore recommend shrinkage methods in full models including prespecified predictors and incorporation of external information, when prognostic models are constructed in small data sets.

Most randomised clinical trials require periodic monitoring of the accumulating data. While the efficiency of trial management is enhanced by data monitoring, ethical reasons should primarily dictate the need to terminate or change a trial in response to interim findings. This article focuses on the ethical dilemma of when to stop a clinical trial and places statistical stopping rules in the context of such ethical decision making. Other issues include the organisation of data monitoring committees and the problems of premature publication and exaggerated estimation in trials that stop early. Several topical examples are used to convey the relevance of these issues to current practice.

ABSTRACT: BACKGROUND: The objective was to review all available literature concerning Type D (distressed) personality among the general population and to discuss its implications for research on health status, disease-promoting mechanisms and work-related problems in non-clinical populations. METHODS: A computerized search of the literature was performed independently and in duplicate by both investigators on December 21st, 2009. Published research reports were included if they studied Type D personality among the general population. Nineteen articles were selected and they were subjected to an 11-item standardised quality checklist by both investigators. RESULTS: The methodological quality of the selected studies was adequate to high. The studies included in this review showed that the presence of Type D characteristics had a negative impact on mental health status (more symptoms of depression, anxiety, post-traumatic stress disorder, mental distress, passive coping, and less social support) and physical health status (more somatic complaints, lower health status, more influenza-like illness reporting). Other studies reported on behavioral and biological mechanisms of disease in apparently healthy individuals with a Type D personality. Finally, some studies also showed a negative effect of Type D personality on work-related problems (higher absence-leave, higher levels of vital exhaustion and burnout, and more work-related stress). CONCLUSIONS: Type D personality is a vulnerability factor for general psychological distress that affects mental and physical health status and is associated with disease-promoting mechanisms and work-related problems in apparently healthy individuals.

ABSTRACT: BACKGROUND: Research has shown strong links between parenting and child psychopathology. The moderating role of child gender is of particular interest, due to gender differences in socialization history and in the prevalence of psychiatric disorders. Currently there is little agreement on how gender moderates the relationship between parenting and child psychopathology. This study attempts to address this lack of consensus by drawing upon two theories (self-salience vs. gender stereotyped misbehaviour) to determine how child gender moderates the role of parenting, if at all. METHODS: Using generalized estimating equations (GEE) associations between three parenting dimensions (hostile-ineffective parenting, parental consistency, and positive interaction) were examined in relationship to child externalizing (physical aggression, indirect aggression, and hyperactivity-inattention) and internalizing (emotional disorder-anxiety) dimensions of psychopathology. A sample 4 and 5 year olds from the National Longitudinal Survey of Children and Youth (NLSCY) were selected for analysis and followed over 6 years (N = 1214). Two models with main effects (Model 1) and main effects plus interactions (Model 2) were tested. RESULTS: No child gender-by-parenting interactions were observed for child physical aggression and indirect aggression. The association between hostile-ineffective parenting and child hyperactivity was stronger for girls, though this effect did not reach conventional levels of statistical significance (p = .059). The associations between parenting and child emotional disorder did vary as a function of gender, where influences of parental consistency and positive interaction were stronger for boys. DISCUSSION: Despite the presence of a few significant interaction effects, hypotheses were not supported for either theory (i.e. self-salience or gender stereotyped misbehaviour). We believe that the inconsistencies in the literature regarding child gender-by-parenting interactions is due to the reliance on gender as an indicator of a different variable which is intended to explain the interactions. This may be problematic because there is likely within-gender and between-sample variability in such constructs. Future research should consider measuring and modelling variables that are assumed to explain such interactions when conducting gender-by-parenting research.

Early diagnosis of language disorders and autism is important, and early intervention for autism and some language disorders makes a difference. Developmental surveillance of children to detect these disorders should be a routine part of medical practice. The persistence and pervasiveness of communication and socialising deficits differentiate children with autism from those with specific developmental language disorders. Hearing and vision assessment is essential in any communication disorder. Interventions, targeted to identified areas of need, should encompass communication enhancement, behavioural therapy, educational modification, parent education and family support. Pharmacological interventions have an important but discrete role in autism, but there are no magic bullets. It is important to remember that the normal childhood illnesses occur in children with developmental disorders. Parents should be directed to reliable websites on the Internet, and given information and books to read as well as phone numbers of relevant services (eg, autism associations). There is a need for increased government financial support for early intervention programs.

BACKGROUND: We examined whether children who are diagnosed as having schizophrenia in adulthood could be distinguished from their peers on performance in elementary school. METHODS: We used a case-control study design nested within a population-based birth cohort of all individuals born in Helsinki, Finland, between January 1, 1951, and December 31, 1960. Case ascertainment was from 3 national health care registers. Elementary school records were obtained for 400 children who were diagnosed as having schizophrenia in adulthood and for 408 controls. Results were analyzed for the 4 years of schooling (ages 7-11 years) that were common to all pupils. School subjects were entered into a principal components analysis and produced 3 factors: academic, nonacademic, and behavioral. These factors were compared between cases and controls after adjusting for sex and social group. Eligibility for high school and progression to high school were investigated among cases and controls. RESULTS: Cases performed significantly worse than controls only on the nonacademic factor (which loaded sports and handicrafts). There were no differences between the groups on the academic or behavioral factors, and there were no significant clinical correlates of factor scores. Cases were significantly less likely than controls to progress to high school, despite similar eligibility. CONCLUSIONS: Poor performance in sports and handicrafts during elementary school, which may indicate a motor coordination deficit, appears to be a risk factor for later schizophrenia. Poor academic performance in elementary school was not a risk factor for schizophrenia in this study. Lack of expected progression to high school among cases, despite good academic grades, provides evidence for deteriorating premorbid functional adjustment in schizophrenia.

Several psychiatric disorders-such as bipolar disorder, schizophrenia and autism-are highly heritable, yet identifying their genetic basis has been challenging, with most discoveries failing to be replicated. However, inroads have been made by the incorporation of intermediate traits (endophenotypes) and of environmental factors into genetic analyses, and through the identification of rare inherited variants and novel structural mutations. Current efforts aim to increase sample sizes by gathering larger samples for case-control studies or through meta-analyses of such studies. More attention on unique families, rare variants, and on incorporating environment and the emerging knowledge of biological function and pathways into genetic analysis is warranted.

The purpose of this article is to reduce potential statistical barriers and open doors to canonical correlation analysis (CCA) for applied behavioral scientists and personality researchers. CCA was selected for discussion, as it represents the highest level of the general linear model (GLM) and can be rather easily conceptualized as a method closely linked with the more widely understood Pearson r correlation coefficient. An understanding of CCA can lead to a more global appreciation of other univariate and multivariate methods in the GLM. We attempt to demonstrate CCA with basic language, using technical terminology only when necessary for understanding and use of the method. We present an entire example of a CCA analysis using SPSS (Version 11.0) with personality data.

Personality differences between Asian American (N = 320) and European American men (N = 242) and also among Asian American ethnic groups (Korean, Chinese, Japanese, Filipino, and mixed Asian) are examined on the Big Five personality dimension. Personality structures for Asian Americans and European Americans closely replicate established norms. However, congruence is greater for European American and highly acculturated Asian American men than for low acculturated Asian American men. Similar patterns are found for the construct loss of face (LOF). Asian American men with a high concern for LOF are less similar in their personality structure to European American men than Asian American men with low LOF concern. Mean differences are also found among Asian American and European American men, who differ significantly on Extraversion, Conscientiousness, Openness, and Neuroticism. Results indicate that acculturation and LOF are significantly associated with these four personality dimensions for both Asian American and European American men.

Standard variance-components quantitative trait loci (QTL) linkage analysis can produce an elevated rate of type 1 errors when applied to selected samples and non-normal data. Here we describe an adjustment of the log-likelihood function based on conditioning on trait values. This leads to a likelihood ratio test that is valid in selected samples and non-normal data, and equal in power to alternative methods for analyzing selected samples that require knowledge of the ascertainment procedure or the trait values of non-selected individuals.

ABSTRACT: BACKGROUND: To confirm the internal structure of the Health Related Quality of Life for Eating Disorders version 2 questionnaire (HeRQoLEDv2) and create and validate a shortened version (HeRQoLED-S). METHODS: 324 patients with eating disorders were assessed at baseline and one year later (75.6% of whom responded). We performed a confirmatory factor analysis of the HeRQoLEDv2 using baseline data, and then a Rasch analysis to shorten the questionnaire. Data obtained at year one was used to confirm the structure of the HeRQoLED short form and evaluate its validity and reliability. RESULTS: Two latent second-order factors - social maladjustment and mental health and functionality - fit the data for the HeRQoLEDv2. Rasch analysis was computed separately for the two latent second-order factors and shortened the HeRQoLEDv2 to 20 items. Infit and outfit indices were acceptable, with the confirmatory factor analysis of the HeRQoLED short form giving a root mean square error of approximation of 0.07, a non-normed fit index and a comparative fit index exceeding 0.90. The validity was also supported by the correlation with the convergent measures: the social maladjustment factor correlated 0.82 with the dieting concern factor of the Eating Attitudes Test-26 and the mental health and functionality factor correlated -0.69 with the mental summary component of the Short Form-12. Cronbach alphas exceeded 0.89. CONCLUSIONS: Two main factors, social maladjustment and mental health and functionality, explain the majority of HeRQoLEDv2 scores. The shortened version maintains good psychometric properties, though it must be validated in independent samples.

BACKGROUND: In recent years, a growing number of methods for synthesising qualitative research have emerged, particularly in relation to health-related research. There is a need for both researchers and commissioners to be able to distinguish between these methods and to select which method is the most appropriate to their situation. DISCUSSION: A number of methodological and conceptual links between these methods were identified and explored, while contrasting epistemological positions explained differences in approaches to issues such as quality assessment and extent of iteration. Methods broadly fall into 'realist' or 'idealist' epistemologies, which partly accounts for these differences. SUMMARY: Methods for qualitative synthesis vary across a range of dimensions. Commissioners of qualitative syntheses might wish to consider the kind of product they want and select their method - or type of method - accordingly.

OBJECTIVE: The aim of this study is to describe the personality disorders (PD) and personality profile of heroin-abusers and their quality of life (QoL), and to investigate the correlation between the two. METHOD: One hundred and eighty heroin-abusers during their residential treatment participated in the study. The Structured Clinical Interview-II (SCID-II) allowed the identification of two subgroups of heroin-abusers on the basis of presence/absence of a PD. All patients filled in the Temperament and Character Inventory (TCI), the McGill QoL Questionnaire (MQOL) and an anamnestic sheet. A control group of 63 non-clinical subjects was recruited. RESULTS: Abusers with a PD differ in their personality profile from abusers without PD and score lower on the total MQOL. As regards TCI scales, novelty seeking (NS), reward dependence (RD) and self-directedness (SD) predict the age of onset of the abuse, while cooperativeness (C) is a predictor of the number of community admissions. DISCUSSION: Low scores on self-directedness and cooperativeness in abusers support the hypothesis of an immature character and relational difficulties. Novelty seeking is the only dimension which is altered both in abusers with and without a PD and is not strictly dependent on Axis II comorbidity. QoL is lower in abusers than in controls, according to their physical, psychological and existential suffering. Last, an interesting link emerged between personality and perceived QoL.

BACKGROUND: This study examined the nature of the relationships among neurocognition, intrinsic motivation, and psychosocial functioning for persons with schizophrenia. Hypotheses concerning both mediator and moderator mechanisms were tested. METHOD: 120 individuals diagnosed with schizophrenia were recruited as they entered outpatient psychosocial rehabilitation programs. Measures of psychosocial functioning and intrinsic motivation were administered at baseline. Measures of neurocognition were administered at baseline by testers blind to scores on other study variables. Data were analyzed using latent construct modeling to test for mediator and moderator effects. RESULTS: There were strong bivariate relationships between neurocognition, intrinsic motivation, and psychosocial functioning. The results demonstrated that intrinsic motivation strongly mediated the relationship between neurocognition and psychosocial functioning. This mediation was evidenced by: (i) the direct path from neurocognition to functional outcome no longer being statistically significant after the introduction of motivation into the model, (ii) the statistical significance of the indirect path from neurocognition through motivation to functional outcome. There was no support for the two moderation hypotheses: the level of neurocognition did not influence the relationship between intrinsic motivation and psychosocial functioning, nor did the level of intrinsic motivation influence the relationship between neurocognition and psychosocial functioning. CONCLUSIONS: Neurocognition influences psychosocial functioning through its relationship with intrinsic motivation. Intrinsic motivation is a critical mechanism for explaining the relationship between neurocognition and psychosocial functioning. Implications for the theoretical understanding and psychosocial treatment of intrinsic motivation in schizophrenia are discussed.

Auditory latent inhibition (LI) and schizotypy were measured in (n=54), showing that LI was an inver- ted-U function of schizotypy score. Only average levels of schizotypy were associated with undiminished LI while both low- and high-SPQ subjects showed reduced LI. No relationship was found between LI and either psychoticism or any of the ve NEO PI-R domains. These results complement the similar complex relationship of neuroleptic drug dose effects on LI in normals and schizophrenics. A priming task and the unusual uses and pattern meanings measures of creativity were related to personality measures of schizo- typy, N, E, and O (but not the EPQ-R psychoticism, LI, or priming performance). Priming effects tracked the inverted-U function of schizotypal personality questionnaire (SPQ) scale scores shown in the LI task. It is suggested that LI is dependent on a non-linear interaction with masking task load and attentional allo- cation, modulated by schizotypy.

In the last two decades, researchers have developed a number of item response models for the analysis of preference data in which the regression between latent trait θ and item responses, P(θ), is single-peaked. As opposed to the monotonic functions such as the logistic function common to IRT for dominance data, these models are probabilistic analogues of Coombs' deterministic unfolding models. One potential barrier to the wider acceptance of such models is the curious fact that most ideal point item response models have bimodal item information functions. Unfortunately, mathematically rigorous explanations for this unusual behavior have not been provided by authors. More broadly, properties of the information function of ideal point IRT models are unknown. This article proves several theorems about the IIFs of ideal point models, in particular, showing that the IIF can be bimodal, unimodal, or singular depending on qualitative characteristics of P (θ), in particular the maximum value of P(θ) and P′′(θ). The importance of these results for test construction is also discussed and illustrated through a simple empirical example.

The value of alexithymia assessments in medical and psychiatric research is well documented, but such assessments in cannabis abusers are scarce. Moreover, despite repeated calls for multimethod alexithymia evaluations, researchers typically use 1 self-report only: the 20-item Toronto Alexithymia Scale. Herein, we evaluated (1) the psychometric properties of the Observer Alexithymia Scale (OAS), (2) the correspondence between 3 alexithymia measures, (3) OAS raters' affect and its relationship to OAS scores, and (4) cannabis abusers' alexithymic features. Eighty-seven cannabis abusers completed self-reports measuring alexithymia (Toronto Alexithymia Scale, Bermond-Vorst Alexithymia Questionnaire-B), depression (13-item Beck Depression Inventory), and anxiety (State and Trait Anxiety Inventory-Form Y) and asked relatives to rate them using the OAS. The raters also completed the self-report scales. The OAS met acceptable reliability and validity standards, with the exception of relatively low interrater reliability for one of its subscales. Rater affect appeared to influence OAS scores, albeit slightly. Patients' OAS scores were higher than scores reported for people-in-general samples and lower than those for outpatient clinical samples. Alexithymia rates were similar to those previously reported in cannabis abusers. Our results demonstrated the adequacy and appropriateness of the OAS in these (and related) clinical samples, which may encourage multimethod alexithymia assessments in both research and clinical practice.

This paper aims to give a general discussion of parametric item response theory models, paying close atten- tion to the Rasch model and its extensions for the analysis of multiple dichotomous and polytomous items. As part of this discussion the paper reviews both the models commonly used in IRT and the procedures utilized to estimate the parameters of these models, and their implications. After giving a general introduction to IRT models the paper examines the appropriateness of these models for the measurement of food security and hunger. Specifically, the paper examines how appropriate IRT is for the analysis of the food security items by examining a subset of data from the 2002 CPS, and then asks the question of whether or not the propensity measured by the food security items is in fact related to true food insecurity. Finally, the paper examines how one might classify survey respondents into one of the three food security classes and/or estimate the proportions of individuals in the population that fall into each of these classes.

A framework is presented for modeling the relational structure of concepts using item response theory (IRT) models with interactions between the items, so-called models with local item dependency (LID). The proposed approach works for unidimensional and multidimensional concepts. For the relational structure of a concept to be analyzed, two types of items are used: items that directly refer to the concept and items that refer to the underlying components. The dependencies (the LIDs) are included in the model to analyze the mutual relations between the components and between the components and the concept. In a study on guilt, it was found that a unidimensional model complemented with situation-specific dependencies could explain the data that were gathered. Because of its flexibility, the approach is a promising tool for a structural analysis of concepts.

The psychometric properties of the newest version of the Temperament and Character Inventory (the TCI-R) were evaluated in a large (n = 727) community sample, as was the TCI-140, a short inventory derivative. Facets-to-scale confirmatory and exploratory factor analyses of the TCI-R did not support the organization of temperament and character facet scales within their superordinate domains. Five of the 29 facet scales also displayed relatively low internal consistency (a < .70). Factor analyses of the TCI-140 item set yielded only limited support for hypothesized item-to-scale memberships. Harm Avoidance, Novelty Seeking, and Self-Directedness items, in particular, were not well differentiated. Although psychometrically comparable, the TCI-R and the TCI-140 demonstrate many of the limitations of earlier inventory versions. Implications associated with the use of the TCI-R and TCI-140 and C. R. Cloninger's theory of personality are discussed.

Although obsessive-compulsive disorder (OCD) has long been a unitary diagnosis, there is much recent interest in its potential heterogeneity, as manifested by symptom subgroups. This study evaluated existing models of symptom structure in a sample of 203 individuals with OCD. Using confirmatory factor analysis, we examined the ability of each model to account for two levels of data: a priori symptom groupings (second-order) and individual symptoms, identified by the Yale-Brown Obsessive Compulsive Scale symptom checklist. Four models were examined: a single-factor, a two-factor (i.e., obsessions and compulsions), and two multidimensional models, comprising three and four factors. Adequate fit was found solely for the four-factor model-specifying obsessions/checking, symmetry/ordering, contamination/cleaning, and hoarding-but only at the second-order level; it did not account for relationships among discrete symptoms. Parameter estimates showed within-factor heterogeneity, as well as overlap between factors, most notably the two representing checking and contamination-related symptoms. The implications of these findings are discussed. Results provide evidence for the multidimensionality of OCD symptoms, but suggest that a comprehensive model has yet to be identified. They also point to the inadequacy of groupings based solely upon overt behavioural similarities (e.g., 'checking'). Recommendations are made for future research.

We have studied the yield of Escherichia coli tRNA(Trp) obtained from in vitro T7 RNA polymerase transcription using incomplete factorial and response surface methods. Incomplete factorial experiments were first used to estimate the relative impact of six variables on the yield of tRNA(Trp). Fifteen trials were performed according to a balanced and randomized design. The correlation between observed yield and all experimental variables was identified by stepwise multiple linear regression analysis. The concentrations of T7 RNA polymerase, DNA template, NTP and MgCl2 proved to be significantly correlated with the yield of tRNA(Trp). We then optimized the yield with respect to each of these four variables simultaneously with a designed, response surface experiment based on the Hardin-Sloane minimum prediction variance algorithm. Twenty experiments were performed, in duplicate, to sample the quadratic surface relating the yield to the four significant variables. Coefficients of the quadratic function with all two-factor interactions were evaluated by stepwise regression using least squares, and significant coefficients were retained. Partial differentiation of the resulting quadratic model showed it to possess an optimum. Transcription performed at the corresponding conditions yielded 6-fold more tRNA(Trp) than the initial conditions, confirming the predictive value of the experimentally determined response surface.

Genetic epidemiologic studies indicate that all ten personality disorders (PDs) classified on the DSM-IV axis II are modestly to moderately heritable. Shared environmental and nonadditive genetic factors are of minor or no importance. No sex differences have been identified, Multivariate studies suggest that the extensive comorbidity between the PDs can be explained by three common genetic and environmental risk factors. The genetic factors do not reflect the DSM-IV cluster structure, but rather: i) broad vulnerability to PD pathology or negative emotionality; ii) high impulsivity/low agreeableness; and iii) introversion. Common genetic and environmental liability factors contribute to comorbidity between pairs or clusters of axis I and axis II disorders. Molecular genetic studies of PDs, mostly candidate gene association studies, indicate that genes linked to neurotransmitter pathways, especially in the serotonergic and dopaminergic systems, are involved. Future studies, using newer methods like genome-wide association, might take advantage of the use of endophenotypes.

The present study investigated whether temperament categories and diagnoses of the Revised Infant Temperament Questionnaire (RITQ) remain stable during infancy. Additionally, the relationships between RITQ ratings and scores on the Brazelton Neonatal Behavioral Assessment Scale and the Bayley Scales of Infant Development were evaluated. The sample included 79 nonrisk infants. The results indicate that most categories of temperament, as well as diagnostic clusters, remain stable from 4 to 8 months of life. The majority of NBAS dimensions and Bayley mental scores were not significantly associated with temperament ratings. The antecedents of ratings of infants' difficultness were similarly unidentified by mothers' perceptions of their infant's behavior.

We examined age differences in attention to and memory for faces expressing sadness, anger, and happiness. Participants saw a pair of faces, one emotional and one neutral, and then a dot probe that appeared in the location of one of the faces. In two experiments, older adults responded faster to the dot if it was presented on the same side as a neutral face than if it was presented on the same side as a negative face. Younger adults did not exhibit this attentional bias. Interactions of age and valence were also found for memory for the faces, with older adults remembering positive better than negative faces. These findings reveal that in their initial attention, older adults avoid negative information. This attentional bias is consistent with older adults' generally better emotional well-being and their tendency to remember negative less well than positive information.

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

Recent studies have suggested that sex-specific genetic architecture could be because of the effects of autosomal genes that are differentially expressed in males and females. Yet, few studies have explored the effects of X-linked genes on sex-specific genetic architecture. In this study, we extended the variance component, maximum likelihood method to evaluate the relative contributions of sex-specific effects on both autosomes and the X chromosome to estimates of heritability of 20 quantitative human phenotypes in the Hutterites. Seventeen of these traits were previously analyzed in this population under a model that did not include X chromosomal effects; three traits are analyzed for the first time (age at menarche, percent fat and fat-free mass [FFM]). Seven traits (systolic blood pressure (SBP), adult height, fasting insulin, triglycerides, lipoprotein (a) [Lp(a)], serotonin, and age at menarche) showed significant X-linked effects; three of these (SBP, adult height, and triglycerides) showed X-linked effects only in males. Four traits (Lp(a), low-density lipoprotein cholesterol, ratio of percent predicted forced expiratory volume at 1 s/forced vital capacity, and FFM) showed significant sex-environment interactions, and two traits (high-density lipoprotein cholesterol and FFM) showed significant sex-specific autosomal effects. Our analyses demonstrate that sex-specific genetic effects may not only be common in human quantitative traits, but also that the X chromosome both plays a large role in these effects and has a variable influence between the sexes.

The authors bring together approaches from cognitive and individual differences psychology to model characteristics of reaction time distributions beyond measures of central tendency. Ex-Gaussian distributions and a diffusion model approach are used to describe individuals' reaction time data. The authors identified common latent factors for each of the 3 ex-Gaussian parameters and for 3 parameters central to the diffusion model using structural equation modeling for a battery of choice reaction tasks. These factors had differential relations to criterion constructs. Parameters reflecting the tail of the distribution (i.e., tau in the ex-Gaussian and drift rate in the diffusion model) were the strongest unique predictors of working memory, reasoning, and psychometric speed. Theories of controlled attention and binding are discussed as potential theoretical explanations.

The procedures for computing augmented subscores described by Wainer et al. (2001) may be thought of as a multi-stage estimation procedure for proficiency estimates within a special case of a multidimensional item response theory (MIRT) model, with as many dimensions as there are subscales. In this paper we describe steps toward a simultaneous (one-stage) estimation system for constrained MIRT models, using Markov chain Monte Carlo (MCMC) approaches to obtain the item parameters and conventional IRT computation to produce scale scores that are augmented subscores. Such a system would provide, in a single integrated analysis, more efficient estimates of subscores for mutually exclusive sets of items (“independent clustering”) that are computed by the Wainer et al. (2001) procedure, as well as subscores for tests in which the assignment of items to subscales is not mutually exclusive (the latter has been referred to as “multi-component mapping”). Combined with modification of a test's item specifications to include items that more specifically measure individual skills, and multi-component mapping, useful subscores (or “skill scores”) may be computable from data obtained with tests very much like current large-scale assessments.

Recently it has become popular to learn sparse Gaussian graphical models (GGMs) by imposing l1 or group l1,2 penalties on the elements of the precision matrix. This penalized likelihood approach results in a tractable convex optimization problem. In this paper, we reinterpret these results as per- forming MAP estimation under a novel prior which we call the group l1 and l1,2 positive- definite matrix distributions. This enables us to build a hierarchical model in which the l1 regularization terms vary depending on which group the entries are assigned to, which in turn allows us to learn block struc- tured sparse GGMs with unknown group as- signments. Exact inference in this hierarchi- cal model is intractable, due to the need to compute the normalization constant of these matrix distributions. However, we derive up- per bounds on the partition functions, which lets us use fast variational inference (optimiz- ing a lower bound on the joint posterior). We show that on two real world data sets (mo- tion capture and financial data), our method which infers the block structure outperforms a method that uses a fixed block structure, which in turn outperforms baseline methods that ignore block structure.

Psychological science has usually approached the treatment of disorder through research on individual combinations of risk and protective factors (including life experiences, thinking styles, behaviors, social relationships and genes) and the application of interventions that focus on improvements in the individual. However, we can do better than this. Not only should we be aiming to enhance well- being rather than merely reducing disorder, but we should also be doing so for the majority of people rather than the few who have a disorder. In this article, I focus on the mental health spectrum and make the case for a broad population- based approach. I argue that a very small shift in the pop- ulation mean of the underlying symptoms or risk factors can do more to enhance well-being and reduce disorder than would any amount of intervention with individuals who need help. Examples from research on alcohol abuse and psy- chological distress are presented to illustrate the value of a population-based approach.

MOTIVATION: Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices. RESULTS: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.

The objectives of this study was to develop and test the psychometric properties of the Performance Skills Questionnaire (PSQ), addressed to measure performance skills of preschoolers, as reported by their parents. Participants included 231 children ranging in age from 4 to 6 years old, with mild to moderate developmental disabilities and 240 children without disabilities at same age range. Internal consistency, test-retest, construct validity, and divergent and convergent validity were assessed. The PSQ has shown good internal reliability, and temporal stability. Construct validity was supported by factor analysis which yielded 3 factors that explained almost 52% of the total variance. Significant differences were found between known groups. Convergent and divergent validity were supported by significant correlations with Visual-Motor Integration (VMI) test, and the Children Participation Questionnaire (CPQ). The PSQ is a unique tool that measures performance skills based on preschool children's everyday function. Results provide evidence in support of the PSQ as a reliable and psychometrically sound instrument.

OBJECTIVE: To translate the child behaviour checklist (CBCL) into Sinhala and validate it for assessment of mental health status of children aged 5-10 years. DESIGN AND SETTING: Translation/back-translation method was used to translate the English CBCL into Sinhala. Each item in the Sinhala CBCL (CBCL-S) was rated by mental health professionals to determine semantics, content, and conceptual validity types. To ascertain criterion validity, total scores obtained for CBCL-S by administering it to parents or parent surrogates of 49 girls and 80 boys aged 5-10 years attending the specialist psychiatry clinics and 69 boys and 69 girls in the same age group from the community were compared with clinical diagnoses by a child psychiatrist. Receiver operator characteristic curves were drawn to obtain the cut-off points in CBCL-S for boys and girls separately. RESULTS: Semantics, content, and conceptual and criterion validity of CBCL-S were satisfactory. At the cut-off level of 39, CBCL-S had a sensitivity of 90% and a specificity of 88% for boys and a sensitivity of 89% and a specificity of 92% for girls. Internal consistency, test-retest reliability, and inter-interviewer reliability of CBCL-S were satisfactory. INTERPRETATION: CBCL-S is a valid and reliable instrument to measure mental health status of Sinhalese children aged 5-10 years in Sri Lanka.

The association of DNA copy-number variation (CNV) with specific gene function and human disease has been long known, but the wide scope and prevalence of this form of variation has only recently been fully appreciated. The latest studies using microarray technology have demonstrated that as much as 12% of the human genome and thousands of genes are variable in copy number, and this diversity is likely to be responsible for a significant proportion of normal phenotypic variation. Current challenges involve developing methods not only for detecting and cataloging CNVs in human populations at increasingly higher resolution but also for determining the association of CNVs with biological function, recent human evolution, and common and complex human disease.

In the current study we examined the links between maternal sensitivity and children's secure attachment in a sample of 45 preschool-age boys with Autism Spectrum Disorders (ASD). We hypothesized that mothers of securely attached children would be more sensitive to their children than mothers of insecurely attached children. Children's attachment was assessed using Ainsworth's Strange Situation Procedure (SSP; Ainsworth, Blehar, Waters, & Wall, 1978). Mothers' sensitivity and children's responsiveness to their mothers were assessed using the Emotional Availability Scales (Biringen, Robinson, & Emde, 1993). The findings supported our hypothesis: mothers of securely attached children were more sensitive to their children even when controlling for the severity of children's diagnosis (Autism Disorder vs. Pervasive Developmental Disorder - Not Otherwise Specified (PDD-NOS)), children's level of functioning (high vs. low), and children's levels of responsiveness. The significance of sensitivity for security of attachment in ASD and the implications of these findings for the validity of the SSP in children with ASD are discussed.

Many applied problems require a covariance matrix estimator that is not only invertible, but also well-conditioned (that is, inverting it does not amplify estimation error). For large- dimensional covariance matrices, the usual estimator-the sample covariance matrix-is typically not well-conditioned and may not even be invertible. This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically. This estimator is distribution-free and has a simple explicit formula that is easy to compute and interpret. It is the asymptotically optimal convex linear combination of the sample covariance matrix with the identity matrix. Optimality is meant with respect to a quadratic loss function, asymptotically as the number of observations and the number of variables go to infinity together. Extensive Monte-Carlo confirm that the asymptotic results tend to hold well in finite sample.

Anorexia nervosa is a severe and complex disorder with incompletely known vulnerability factors. It is generally recognized that anorexia nervosa is a familial disorder, but the majority of twin studies have shown that the concordance rate for monozygotic twins is higher (on average 44%) than for dizygotic twins (on average 12.5%). This difference in concordance rates shows that genetic factors, more than common familial environment, may explain why the 'anorexia nervosa' phenotype runs in families. In order to estimate the heritability in the broad sense of anorexia nervosa according to published familial and twin studies, we first assessed the intrapair correlation between monozygotic and dizygotic twins, and secondly calculated the deviation threshold of relatives of affected probands from the relative mean. In this review, we obtained an estimation of the heritability at 0.72 according to all published controlled familial studies (six references quoted in MEDLINE(R)), and 0.71 for all published twin studies (59 references quoted in MEDLINE(R)). This estimation is close to the ones previously proposed, between 0. 5 and 0.8. Familial and twin studies may also help to define the boundaries of the phenotype, shedding light on the complex relationship between anorexia nervosa on the one hand, and bulimia nervosa, mood disorders, and alcoholism on the other. Demonstrating the importance of genetic factors in anorexia nervosa, and more specifically for anorexia of the restrictive type, requires not only prospective and adoption studies (which are still lacking), but also genetic polymorphisms analyses, which began very recently.

OBJECTIVE: To examine the multivariate nature of risk factors for youth violence including delinquent peer associations, exposure to domestic violence in the home, family conflict, neighborhood stress, antisocial personality traits, depression level, and exposure to television and video game violence. STUDY DESIGN: A population of 603 predominantly Hispanic children (ages 10-14 years) and their parents or guardians responded to multiple behavioral measures. Outcomes included aggression and rule-breaking behavior on the Child Behavior Checklist (CBCL), as well as violent and nonviolent criminal activity and bullying behavior. RESULTS: Delinquent peer influences, antisocial personality traits, depression, and parents/guardians who use psychological abuse in intimate relationships were consistent risk factors for youth violence and aggression. Neighborhood quality, parental use of domestic violence in intimate relationships, and exposure to violent television or video games were not predictive of youth violence and aggression. CONCLUSION: Childhood depression, delinquent peer association, and parental use of psychological abuse may be particularly fruitful avenues for future prevention or intervention efforts.

Two forces motivate this special section, "New Methods for New Questions in Developmental Psychology." First are recent developments in social science methodology and the increasing availability of those methods in common software packages. Second, at the same time psychologists' understanding of developmental phenomena has continued to grow. At their best, these developments in theory and methods work in tandem, fueling each other. Newer methods make it possible for scientists to better test their ideas; better ideas lead methodologists to techniques that better reflect, capture, and quantify the underlying processes. The articles in this special section represent a sampling of these new methods and new questions. The authors describe common themes in these articles and identify barriers to future progress, such as the lack of data sharing by and analytical training for developmentalists.

Differential item functioning (DIF) occurs when an item on a test or question- naire has different measurement properties for 1 group of people versus another, irrespective of mean differences on the construct. This study focuses on the use of multiple-indicator multiple-cause (MIMIC) structural equation models for DIF testing, parameterized as item response models. The accuracy of these methods, and the sample size requirements, are not well established. This study examines the accuracy of MIMIC methods for DIF testing when the focal group is small and compares results with those obtained using 2-group item response theory (IRT). Results support the utility of the MIMIC approach. With small focal- group samples, tests of uniform DIF with binary or 5-category ordinal responses were more accurate with MIMIC models than 2-group IRT. Recommendations are offered for the application of MIMIC methods for DIF testing.

Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.

BACKGROUND: Standardised measuring instruments are increasingly used in psychiatric research cross-culturally. These instruments are considered to be culturally equivalent when all forms of biases, or social norms specific to the culture of origin, have been removed. OBJECTIVES: To describe the qualitative process of selection, translation and cultural adaptation of a mental health battery for use in a Xhosa-speaking community that is, as far as possible, 'culture-free' or equivalent. METHOD: Informal discussions were held with key members in the community to determine what would be considered as appropriate for the community in respect of psychiatric screening instruments. Existing rating-scales for depression, alcohol abuse and posttraumatic stress disorder that would meet these criteria were identified and then translated from English into Xhosa. Cultural equivalence was achieved by combining methods of back-translation, committee consensus approach and decentering. Discussions during the committee consensus meetings were recorded and categorized into themes. Two themes emerged: (1) issues related to the attainment of semantic equivalence and (2) broader problems inherent in cross-cultural research. RESULTS: Issues related to individual questionnaires included the use of terms to describe emotional distress cross-culturally. Broader issues related to the translation process itself included the form of language to be used, time-frames, and the use of Likert-scales. It also demonstrated the problems inherent in the categorization of emotions. CONCLUSION: A method of combining a group approach, back-translation, and decentering was effective and efficient in this context for establishing content and semantic equivalence. Cross-cultural adaptation can never completely remove all forms of bias from a research instrument, but such limitations should be acknowledged and openly discussed, rather than hidden or ignored.

Rasch models are characterised by sufficient statistics for all parameters. In the Rasch unidimen- sional model for two ordered categories, the parameterisation of the person and item is symmetrical and it is readily established that the total scores of a person and item are sufficient statistics for their respective parameters. In contrast, in the unidimensional polytomous Rasch model for more than two ordered cate- gories, the parameterisation is not symmetrical. Specifically, each item has a vector of item parameters, one for each category, and each person only one person parameter. In addition, different items can have different numbers of categories and, therefore, different numbers of parameters. The sufficient statistic for the parameters of an item is itself a vector. In estimating the person parameters in presently available software, these sufficient statistics are not used to condition out the item parameters. This paper derives a conditional, pairwise, pseudo-likelihood and constructs estimates of the parameters of any number of persons which are independent of all item parameters and of the maximum scores of all items. It also shows that these estimates are consistent. Although Rasch's original work began with equating tests us- ing test scores, and not with items of a test, the polytomous Rasch model has not been applied in this way. Operationally, this is because the current approaches, in which item parameters are estimated first, cannot handle test data where there may be many scores with zero frequencies. A small simulation study shows that, when using the estimation equations derived in this paper, such a property of the data is no impediment to the application of the model at the level of tests. This opens up the possibility of using the polytomous Rasch model directly in equating test scores.

A test of the performance of two probabilistic classifiers (random forests and multinomial logit models) in automatically defining cancer cases has been carried out on 5608 subjects, registered by the Venetian Tumour Registry (RTV) during the years 1987-1996 and manually checked for possible second cancers that occurred during the 1997-1999 period. An eightfold cross-validation was performed to estimate the classification error; 63 predictive variables were entered into the model fitting. The random forest allows to automatically classify 45% of subjects with a classification error lower than 5%, while the corresponding error is 31% for the multilogit model. The performance of the former classifier is appealing, indicating a potential drop of manually checked cases from 1750 to 960 per incidence year with a moderate error rate. This result suggests to refine the approach and extend it to other categories of manually treated cases.

Imputation is a method of dealing with missing data points by filling in values. In genetics, imputation generally refers to the substitution of missing SNP values. Missing SNP values commonly cause data to be thrown out, as re- genotyping is limited by financial constraints. Recovery of SNP values can keep costs down and restore power lost due to missing data. The linkage disequilibrium (LD) method imputes the value of missing SNPs based on LD correlation data between missing SNPs and SNPs which have been measured successfully. This approach is easy to implement, is generalizable, has decent accuracy, and is fairly fast. On the other hand, it does not have optimal accuracy, and makes decisions without an explicit statistical confidence value.

ABSTRACT: BACKGROUND: Although maternal common mental disorder (CMD) appears to be a risk factor for infant undernutrition in South Asian countries, the position in sub-Saharan Africa (SSA) is unclear METHODS: A population-based cohort of 1065 women, in the third trimester of pregnancy, was identified from the demographic surveillance site (DSS) in Butajira, to investigate the effect of maternal CMD on infant undernutrition in a predominantly rural Ethiopian population. Participants were interviewed at recruitment and at two months post-partum. Maternal CMD was measured using the locally validated Self-Reported Questionnaire (score of [greater than or equal to] six indicating high levels of CMD). Infant anthropometry was recorded at six and twelve months of age. Result: The prevalence of CMD was 12% during pregnancy and 5% at the two month postnatal time-point. In bivariate analysis antenatal CMD which had resolved after delivery predicted underweight at twelve months (OR = 1.71; 95% CI: 1.05, 2.50). There were no other statistically significant differences in the prevalence of underweight or stunted infants in mothers with high levels of CMD compared to those with low levels. The associations between CMD and infant nutritional status were not significant after adjusting for pre-specified potential confounders. CONCLUSION: Our negative finding adds to the inconsistent picture emerging from SSA. The association between CMD and infant undernutrition might be modified by study methodology as well as degree of shared parenting among family members, making it difficult to extrapolate across low- and middle-income countries.

OBJECTIVE: The serotonin transporter gene (SLC6A4) is a strong autism candidate gene because of its association with anxiety, aggression and attention, and the effectiveness of selective serotonin reuptake inhibitors (SSRIs) in treating certain behavioral symptoms. In families with individuals with autism, several reports of biased transmission of both alleles (short, long) at the serotonin transporter gene promotor polymorphism (5-HTTLPR) locus of SLC6A4 now exist. The heterogeneity in these reports may be due to clinical heterogeneity. The authors hypothesized that 5-HTTLPR genotypes would be related to variation in specific symptoms in children with autism. METHOD: The authors explored whether variants of two functional polymorphisms of SLC6A4 (5-HTTLPR, intron 2 variable number tandem repeat [2 VNTR]) were related to behavioral characteristics measured by the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule. Subjects (N=73, age 3-19 years old) met diagnostic criteria for autistic disorder based on both measures. RESULTS: Evidence of genotype-phenotype interactions on the Autism Diagnostic Interview-Revised was found with the 5-HTTLPR short group of HTTLPR (S/L or S/S genotypes) being rated as more severe on the subdomain "failure to use nonverbal communication to regulate social interaction," and the long group (L/L genotype) being more severe on the subdomain "stereotyped and repetitive motor mannerisms" and on an aggression measure. In contrast, on the Autism Diagnostic Observation Schedule, the long group was associated with greater severity on directed facial expressions and unusual sensory interests. There were no significant relationships between the intron 2 VNTR genotypes and subdomains or domains of symptoms on the Autism Diagnostic Interview-Revised or the Autism Diagnostic Observation Schedule. CONCLUSIONS: These findings provide initial support for genotype-specific phenotypes for 5-HTTLPR in autism based on ratings from the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule.

The SF-36 Health Survey is the most widely used self-report measure of functional health. It is commonly used in both randomized controlled trials (RCT) and non-controlled evaluation of medical or other health services. However, determining a clinically significant change in SF-36 outcomes from pre-to-post-intervention, in contrast to statistically significant differences, is often not a focus of medical outcomes research. We propose use of the Reliable Change Index (RCI) in combination with SF-36 norms as one method for researchers, provider groups, and health care policy makers to determine clinically significant healthcare outcomes when the SF-36 is used as a primary measure. The RCI is a statistic that determines the magnitude of change score necessary of a given self-report measure to be considered statistically reliable. The RCI has been used to determine clinically significant change in mental health and behavioral medicine outcomes research, but is not widely applied to medical outcomes research. A usable table of RCIs for the SF-36 has been calculated and is presented. Instruction and a case illustration of how to use the RCI table is also provided. Finally, limitations and cautionary guidelines on using SF-36 norms and the RCI to determine clinically significant outcome are discussed.

The primate neocortex is characterized by a highly expanded supragranular layer (SGL). The interareal connectivity of the neurons in the SLG largely determines the cortical hierarchy that constrains information flow through the cortex. Interareal connectivity is made by precise numbers of connections, raising the possibility that the physiology of a target area is dictated by the numbers of connections and hierarchical distance in each of the pathways that it receives. The developmental mechanisms ensuring the precision of these interareal networks is in part determined by (i) the numbers of SGL neurons generated by the OSVZ, a primate-specific germinal zone. Neuron generation rate in the OSVZ is determined by regulation of the G1 phase of the cell-cycle. This regulation is area-specific and is linked to thalamic projections to the OSVZ; (ii) Prolonged pre- and postnatal pruning of connections originating from the SGL when the infant monkey visually explores its environment. Remodelling serves to sharpen initial patterns of connections and establishes the adult hierarchy. These results suggest that primate cortical networks underlying high-level function undergo prolonged self-organization via regressive phenomena in the cortical plate (axon elimination) and progressive phenomena (directed growth of cortical axons).

We tested whether telomere length is altered in the brains of patients diagnosed with major depression (MD), bipolar disorder (BD) and schizophrenia (SZ) by measuring mean telomere length (mTL) with real-time PCR. The samples are cerebellar gray matter from 46 SZ, 46 BP, and 15 MD patients, and 48 healthy controls. We found no difference in mTL between SZ and controls, BD and controls, MD and controls, or all cases and controls; no correlation between mTL and age was observed, either. This suggests that brain gray matter is unlikely to be related to the telomere length shortening reported in blood of psychiatric patients. White matter deserves further investigation as it has been reported to have a different mTL dynamic from gray matter. Since mTL has been reported to be a heritable quantitative trait, we also carried out genome-wide mapping of genetic factors for mTL, treating mTL as a quantitative trait. No association survived correction of multiple testing for the number of SNPs studied. The previously reported rs2630578 (BICD1) association was not replicated. This suggests that telomere length of cerebellar gray matter is determined by multiple loci with "weak effects."

The importance of predictors is characterized by the extent to which their use reduces uncertainty about predicting the response variable, namely their information importance. The uncertainty associated with a probability distribution is a concave function of the density such that its global maximum is a uniform distribution reflecting the most difficult prediction situation. Shannon entropy is used to operationalize the concept. For nonstochastic predictors, maximum entropy characterization of probability distributions provides measures of information importance. For stochastic predictors, the expected entropy difference gives measures of information importance, which are invariant under one-to-one transformations of the variables. Applications to various data types lead to familiar statistical quantities for various models, yet with the unified interpretation of uncertainty reduction. Bayesian inference procedures for the importance and relative importance of predictors are developed. Three examples show applications to normal regression, contingency table, and logit analyses.

BACKGROUND: The self-report version of the Royal Free Interview for Religious and Spiritual Beliefs has been confirmed as a valid and reliable scale, assessing the manner and nature in which spiritual beliefs are expressed. The aim of the present study was to evaluate the test-retest reliability and psychometric properties of the Greek version of the Royal Free Interview for Religious and Spiritual Beliefs. METHODS: A total of 209 persons (77 men and 132 women) with a mean age of 28.33 +/- 9.44 years participated in the study (test group). We subsequently approached 139 participants of the test group with a mean age of 28.93 +/- 9.60 years, who were asked to complete the Royal Free Questionnaire a second time two weeks later (retest group). RESULTS: The vast majority of participants (58.9%) reported both a religious and a spiritual belief, compared to 52 (25.1%) who told of a religious belief only. The internal consistency of the spiritual scale for the test group proved to be good, as standardized inter-item reliability / Cronbach's alpha was 0.83. Item-total correlations ranged from 0.51 to 0.73. They indicated very good levels of differentiation, thus showing that the questions were appropriate. Internal consistency of the spiritual scale for the retest group proved as good as for the test group. Standardized inter-item reliability / Cronbach's alpha was 0.84. Item-total correlations ranged from 0.52 to 0.75. The Pearson correlation coefficient for the total test-retest score of the spiritual scale was 0.754 (p < 0.001). CONCLUSION: The Greek version of the Royal Free Interview for Religious and Spiritual Beliefs is reliable and thus suitable for use in Greece.

While statistical procedures are well-known for comparing hierarchically related (nested) mean and covariance structure models, statistical tests for comparing non-hierarchically related (nonnested) models have proven more elusive. Though isolated attempts at statistical tests of non-hierarchically related models have been made, none exists within the commonly-used maximum likelihood estimation framework, thereby compromising these methods' accessibility and general applicability. Building upon general theory developed by Vuong (1989) and techniques for establishing the relation between covariance structure models (Raykov & Penev, 1999), the current work provides a general paradigm for conducting statistical tests on competing mean and covariance structure models. The proposed framework is appropriate for hierarchically related models as well as non-hierarchically related models. In developing the structure of the framework, key aspects of model equivalence, relation, and comparison are unified. An illustration demonstrates its use.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

BACKGROUND: Seasonal rhythms in mood and behavior (seasonality) have been reported to occur in the general population. Seasonal affective disorder, a clinically diagnosed syndrome, is believed to represent the morbid extreme of a spectrum of seasonality. Two types of seasonality have been clinically described: one characterized by a winter pattern and a second by a summer pattern of depressive mood disturbance. METHODS: By using methods of univariate and multivariate genetic analysis, we examined the relative contribution of genetic and environmental factors to the risk of seasonality symptoms that were assessed by a mailed questionnaire of 4639 adult twins from a volunteer-based registry in Australia. RESULTS: Seasonality was associated with a winter rather than a summer pattern of mood and behavioral change. In each behavioral domain (ie, mood, energy, social activity, sleep, appetite, and weight), a significant genetic influence on the reporting of seasonal changes was found. Consistent with the hypothesis of a seasonal syndrome, genetic effects were found to exert a global influence across all behavioral changes, accounting for at least 29% of the variance in seasonality in men and women. CONCLUSIONS: There is a tendency for seasonal changes in mood and behavior to run in families, especially seasonality of the winter type, and this is largely due to a biological predisposition. These findings support continuing efforts to understand the role of seasonality in the development of mood disorders.

In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression data.

Numerous studies suggest that seasonal birth may play a pathogenic role in the development of mental disorders. A birth excess of 10% during winter and spring has been shown in schizophrenia. The few studies carried out on affective disorders revealed a significant increase of births in the first quarter of the year in bipolar disorders and major depressive disorder. Subjects with seasonal affective disorder show a peak of births in May. Data on personality, eating and 'neurotic' disorders are less consistent. At the moment there are no data in the literature about anxiety disorders.

ABSTRACT: BACKGROUND: The use of short screening questionnaires may be a promising option for identifying children at risk for depression in a community setting. The objective of this study was to assess the validity of the Short Mood and Feelings Questionnaire (SMFQ) and one- and two-item screening instruments for depressive disorders in a school-based sample of young adolescents. METHODS: Participants were 521 sixth-grade students attending public middle schools. Child and parent versions of the SMFQ were administered to evaluate the child's depressive symptoms. The presence of any depressive disorder during the previous month was assessed using the Diagnostic Interview Schedule for Children (DISC) as the criterion standard. First, we assessed the diagnostic accuracy of child, parent, and combined scores of the full 13-item SMFQ by calculating the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. The same approach was then used to evaluate the accuracy of a two-item scale consisting of only depressed mood and anhedonia items, and a single depressed mood item. RESULTS: The combined child + parent SMFQ score showed the highest accuracy (AUC = 0.86). Diagnostic accuracy was lower for child (AUC = 0.73) and parent (AUC = 0.74) SMFQ versions. Corresponding versions of one- and two-item screens had lower AUC estimates, but the combined versions of the brief screens each still showed moderate accuracy. Furthermore, child and combined versions of the two-item screen demonstrated higher sensitivity (although lower specificity) than either the one-item screen or the full SMFQ. CONCLUSIONS: Under conditions where parents accompany children to screening settings (e.g. primary care), use of a child + parent version of the SMFQ is recommended. However, when parents are not available, and the cost of a false positive result is minimal, then a one- or two-item screen may be useful for initial identification of at-risk youth.

Partial least squares (PLS) path modeling has found increased applications in customer satisfaction analysis thanks to its ability to handle complex models. A modified PLS path modeling algorithm together with a model building strategy are introduced and applied to customer satisfaction analysis at the French energy supplier Electricité de France. The modified PLS algorithm handles all kinds of scales (categorical or nominal variables) and is well suited when nominal or binary variables are involved. PLS path modeling and structural equation modeling are confirmatory approaches and thus need an initial conceptual model. A two-step model building strategy is presented; the first step is based on Bayesian networks structure learning to build the measurement model and the second step is based on partial correlation and hypothesis tests to build the structural model. Applications to customer satisfaction data are presented.

Data from general population surveys (n = 1483 to 9151) in nine European countries (Denmark, France, Germany, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) were analyzed to cross-validate the selection of questionnaire items for the SF-12 Health Survey and scoring algorithms for 12-item physical and mental component summary measures. In each country, multiple regression methods were used to select 12 SF-36 items that best reproduced the physical and mental health summary scores for the SF-36 Health Survey. Summary scores then were estimated with 12 items in three ways: using standard (U.S.-derived) SF-12 items and scoring algorithms; standard items and country-specific scoring; and country-specific sets of 12 items and scoring. Replication of the 36-item summary measures by the 12-item summary measures was then evaluated through comparison of mean scores and the strength of product-moment correlations. Product-moment correlations between SF-36 summary measures and SF-12 summary measures (standard and country-specific) were very high, ranging from 0.94-0.96 and 0.94-0.97 for the physical and mental summary measures, respectively. Mean 36-item summary measures and comparable 12-item summary measures were within 0.0 to 1.5 points (median = 0.5 points) in each country and were comparable across age groups. Because of the high degree of correspondence between summary physical and mental health measures estimated using the SF-12 and SF-36, it appears that the SF-12 will prove to be a practical alternative to the SF-36 in these countries, for purposes of large group comparisons in which the focus is on overall physical and mental health outcomes.

Decision-making is an important function that can be quantified using a two-choice prediction task. Individuals with Autistic Disorder (AD) often show highly restricted and repetitive behavior that may interfere with adaptive decision-making. We assessed whether AD adults showed repetitive behavior on the choice task that was unaffected by changing task demands, by examining the influence of experimenter-determined error rates on decision-making. Sixteen AD adults and 14 typically developed subjects were administered a two-choice task using three error rate conditions. Although AD subjects showed occurrences of stereotyped responding, their decision-making behavior was strongly affected by changes in task demands, especially when they experienced frequent success. Thus, behavioral paradigms that provide frequent reinforcement may be helpful in modifying decision-making abilities in AD.

The aim of the study is to compare the expression level of candidate genes between patients suffering from a severe major depressive episode (MDE) and controls, and also among patients during MDE evolution. After a comprehensive review of the biological data related to mood disorders, we initiated a hypothesis-driven exploration of candidate mRNAs. Using RT-qPCR, we analyzed peripheral blood mononuclear cells (PBMCs) mRNA obtained from a homogeneous population of 11 patients who suffered from severe melancholic MDE. To assess the evolution of MDE, we analyzed PBMC mRNAs that were collected on Day 1 and 8 weeks later. Data from these patient samples were analyzed in comparison to age- and sex-matched healthy controls. Among 40 candidate genes consistently transcribed in PBMCs, 10 were differentially expressed in at least one comparison. We found that variations of mRNA levels for NRG1, SORT1 and TPH1 were interesting state-dependent biological markers of the disease. We also observed that variations in other mRNA expression were associated with treatment efficacy or clinical improvement (CREB1, HDAC5, HSPA2, HTR1B, HTR2A, and SLC6A4/5HTT). Significantly, 5HTT exhibited a strong correlation with clinical score evolution. We also found a state-independent marker, IL10. Moreover, the analysis of 2 separate MDEs concerning a same patient revealed comparable results for the expression of CREB1, HSPA2, HTR1B, NRG1 and TPH1. Overall, our results indicate that PBMCs obtained at different time points during MDE progression represent a promising avenue to discover biological markers for depression.

The problem of testing for genotype-phenotype association with loci on the X chromosome in mixed-sex samples has received surprisingly little attention. A simple test can be constructed by counting alleles, with males contributing a single allele and females 2. This approach does assume not only Hardy-Weinberg equilibrium in the population from which the study subjects are sampled but also, perhaps, an unrealistic alternative hypothesis. This paper proposes 1 and 2 degree-of-freedom tests for association which do not assume Hardy-Weinberg equilibrium and which treat males as homozygous females. The proposed method remains valid when phenotype varies between sexes, provided the allele frequency does not, and avoids the loss of power resulting from stratification by sex in such circumstances.

DETECT is a nonparametric “full” dimensionality assessment procedure that clusters dichotomously scored items into dimensions and provides a DETECT index of magnitude of multidimensionality. Four factors (test length, sample size, item response theory [IRT] model, and DETECT index) were manipulated in a Monte Carlo study of bias, standard error, and root mean square error (RMSE) under the condition of unidimensionality. Bias, standard error, and RMSE of both DETECT indices increased as test length and sample size decreased. Results suggest that the cross-validated index should always be preferred over the exploratory index, even for 100 examinees and five items. Bias, standard error, and RMSE may be problematic for both indices under certain conditions of small samples or short tests. A Monte Carlo procedure could be built into DETECT to estimate and adjust for potential bias.

cluML, a free, open, XML-based format, is a new markup language for microarray data clustering and cluster validity assessment. This format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. The approach described performs an effective tool to support biomedical knowledge representation in gene expression data analysis. Even though cluML was developed for DNA microarray analysis applications, it may be effectively used for the representation of clustering and validation of other biomedical and physical data with no limitations.

ABSTRACT: BACKGROUND: It is arguable that modification of diet, given its potential for positive health outcomes, should be widely advocated and adopted. However, food intake, as a basic human need, and its modification may be accompanied by sensations of both pleasure and despondency and may consequently affect to quality of life (QoL). Thus, the feasibility and success of dietary changes will depend, at least partly, on whether potential negative influences on QoL can be avoided. This is of particular importance in the context of dietary intervention studies and in the development of new food products to improve health and well being. Instruments to measure the impact of nutrition on quality of life in the general population, however, are few and far between. Therefore, the aim of this project was to develop an instrument for measuring QoL related to nutrition in the general population. Methods and results: We recruited participants from the general population and followed standard methodology for quality of life instrument development (identification of population, item selection, n = 24; item reduction, n = 81; item presentation, n = 12; pretesting of questionnaire and initial validation, n = 2576; construct validation n = 128; and test-retest reliability n = 20). Of 187 initial items, 29 were selected for final presentation. Factor analysis revealed an instrument with 5 domains. The instrument demonstrated good cross-sectional divergent and convergent construct validity when correlated with scores of the 8 domains of the SF-36 (ranging from -0.078 to 0.562, 19 out of 40 tested correlations were statistically significant and 24 correlations were predicted correctly) and good test-retest reliability (intra-class correlation coefficients from 0.71 for symptoms to 0.90). CONCLUSIONS: We developed and validated an instrument with 29 items across 5 domains to assess quality of life related to nutrition and other aspects of food intake. The instrument demonstrated good face and construct validity as well as good reliability. Future work will focus on the evaluation of longitudinal construct validity and responsiveness.

Self-organizingmaps(SOM)havebeenrecognizedasapowerfultool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity mea- sure and update method of map evolution for both numeric and categorical fea- tures simultaneously.

Thisreviewpresentsempiricalresearcherswithrecentadvances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that un- derly all causal inferences, the languages used in formulating those assump- tions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coher- ent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interven- tions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attri- bution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.

ABSTRACT : INTRODUCTION : Physical function is a key component of patient-reported outcome (PRO) assessment in rheumatology. Modern psychometric methods, such as Item Response Theory (IRT) and Computerized Adaptive Testing, can materially improve measurement precision at the item level. We present the qualitative and quantitative item-evaluation process for developing the Patient Reported Outcomes Measurement Information System (PROMIS) Physical Function item bank. METHODS : The process was stepwise: we searched extensively to identify extant Physical Function items and then classified and selectively reduced the item pool. We evaluated retained items for content, clarity, relevance and comprehension, reading level, and translation ease by experts and patient surveys, focus groups, and cognitive interviews. We then assessed items by using classic test theory and IRT, used confirmatory factor analyses to estimate item parameters, and graded response modeling for parameter estimation. We retained the 20 Legacy (original) Health Assessment Questionnaire Disability Index (HAQ-DI) and the 10 SF-36's PF-10 items for comparison. Subjects were from rheumatoid arthritis, osteoarthritis, and healthy aging cohorts (n = 1,100) and a national Internet sample of 21,133 subjects. RESULTS : We identified 1,860 items. After qualitative and quantitative evaluation, 124 newly developed PROMIS items composed the PROMIS item bank, which included revised Legacy items with good fit that met IRT model assumptions. Results showed that the clearest and best-understood items were simple, in the present tense, and straightforward. Basic tasks (like dressing) were more relevant and important versus complex ones (like dancing). Revised HAQ-DI and PF-10 items with five response options had higher item-information content than did comparable original Legacy items with fewer response options. IRT analyses showed that the Physical Function domain satisfied general criteria for unidimensionality with one-, two-, three-, and four-factor models having comparable model fits. Correlations between factors in the test data sets were > 0.90. CONCLUSIONS : Item improvement must underlie attempts to improve outcome assessment. The clear, personally important and relevant, ability-framed items in the PROMIS Physical Function item bank perform well in PRO assessment. They will benefit from further study and application in a wider variety of rheumatic diseases in diverse clinical groups, including those at the extremes of physical functioning, and in different administration modes.

Novelty-seeking personality traits are a major risk factor for the development of drug abuse and other unsafe behaviors. Rodent models of temperament indicate that high novelty responding is associated with decreased inhibitory autoreceptor control of midbrain dopamine neurons. It has been speculated that individual differences in dopamine functioning also underlie the personality trait of novelty seeking in humans. However, differences in the dopamine system of rodents and humans, as well as the methods for assessing novelty responding/seeking across species leave unclear to what extent the animal models inform our understanding of human personality. In the present study we examined the correlation between novelty-seeking traits in humans and D(2)-like (D(2)/D(3)) receptor availability in the substantia nigra/ventral tegmental area. Based on the rodent literature we predicted that novelty seeking would be characterized by lowered levels of D(2)-like (auto)receptor availability in the midbrain. Thirty-four healthy adults (18 men, 16 women) completed the Tridimensional Personality Questionnaire-Novelty-Seeking Scale and PET scanning with the D(2)/D(3) ligand [(18)F]fallypride. Novelty-Seeking personality traits were inversely associated with D(2)-like receptor availability in the ventral midbrain, an effect that remained significant after controlling for age. We speculate that the lower midbrain (auto)receptor availability seen in high novelty seekers leads to accentuated dopaminergic responses to novelty and other conditions that induce dopamine release.

Interest in end-of-year accountability exams has increased dramatically since the passing of the NCLB law in 2001. This push has impacted educational research in a wide variety of ways, including a strong desire to be able to model student work in order to make conclu- sive statements about what students know and how this relates to how they will perform on end-of-year standardized exams. This thesis will look at using item response theory (IRT) to estimate student proficiency. This estimated proficiency will then be used to build predic- tion models for end-of-year exam scores. Next, methods to improve a skills model will be explored. Models that account for learning over time will then be considered. Finally, I will compare various different approaches to modeling response data.

OBJECTIVE: To compare the sensitivity to change of two HIV-health-related quality of life (HRQoL) questionnaires-the Medical Outcomes Study (MOS-HIV) and Multidimensional Quality of Life (MQOL-HIV) for use in clinical research. METHODS: A sample of 296 HIV-infected patients starting or switching antiretroviral treatment were randomly assigned either the MOS-HIV or MQOL-HIV questionnaires at baseline and after 3 months of treatment. Ceiling and floor effects were evaluated. Sensitivity to change was assessed by comparing the percentage of dimensions with statistically significant pre-post-treatment changes and the effect sizes in those groups of patients who reported improvement and no change in self-report questions (overall, physical, mental and social health status) and clinical characteristics (number of opportunistic infections, number of symptoms, viral load level and CD4+ count). RESULTS: Ceiling effects were found in HRQoL scores at baseline and after 3 months of treatment in Pain (42.3-41.6%), Role Function (73.1-77.6%) and Social Function (60.9-63%) on MOS-HIV subscales, and in Social Support (38.2-37.6%) and Partner Intimacy (38.2-33.7%) on MQOL-HIV. For patients who improved in self-reported and objective clinical indicators of health status, mean percentage of dimensions with statistically significant pre-post-treatment changes was 86.4% on MOS-HIV and 50% on MQOL-HIV, where mean standardized effect size was 0.45 on MOS-HIV and 0.33 on MQOL-HIV for the total of dimensions. CONCLUSIONS: Based on sensitivity to change the results suggest that for 3 months both questionnaires can be used, but the MOS-HIV is more sensitive than the MQOL-HIV for use in clinical research.

Cognitive interviewing has emerged as one of the more prominent methods for identifying and correcting problems with survey questions. We define cognitive interviewing as the administration of draft survey questions while collecting additional verbal information about the survey responses, which is used to evaluate the quality of the response or to help determine whether the question is generating the information that its author intends. But beyond this general categorization, cognitive interviewing potentially includes a variety of activities that may be based on different assumptions about the type of data that are being collected and the role of the interviewer in that process. This synthesis reviews the range of current cognitive interviewing practices, focusing on three considerations: (1) what are the dominant paradigms of cognitive interviewing-what is produced under each, and what are their apparent advantages; (2) what key decisions about cognitive interview study design need to be made once the general approach is selected (e.g., who should be interviewed, how many interviews should be conducted, and how should probes be selected), and what bases exist for making these decisions; and (3) how cognitive interviewing data should be evaluated, and what standards of evidence exist for making questionnaire design decisions based on study findings. In considering these issues, we highlight where standards for best practices are not clearly defined, and suggest broad areas worthy of additional methodological research.

Understanding differences in the genetic architecture of complex traits between the two sexes has significant implications for evolutionary studies and clinical diagnosis. However, our knowledge about sex-specific genetic architecture is limited largely because of a lack of analytical models that can detect and quantify the effects of sex on the complexity of quantitative genetic variation. Here, we derived a statistical model for mapping DNA sequence variants that contribute to sex-specific differences in allele frequencies, linkage disequilibria, and additive and dominance genetic effects due to haplotype diversity. This model allows a genome-wide search for functional haplotypes and the estimation and test of haplotype by sex interactions and sex-specific heritability. The model, validated by simulation studies, was used to detect sex-specific functional haplotypes that encode a pain sensitivity trait in humans. The model could have important implications for mapping complex trait genes and studying the detailed genetic architecture of sex-specific differences.

BACKGROUND: In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines. RESULTS: We compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results. CONCLUSION: sPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.

This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement learning. It argues that prediction risk is most effectively incorporated by scaling the prediction errors. This way, the learning rate needs adjusting only when the covariance between optimal predictions and past (scaled) prediction errors changes. Evidence is discussed that suggests that the dopaminergic system in the (human and nonhuman) primate brain encodes prediction risk, and that prediction errors are indeed scaled with prediction risk (adaptive encoding).

This study investigated the utility of confirmatory factor analysis (CFA) and item response theory (IRT) models for testing the comparability of psychological measurements. Both procedures were used to investigate whether mood ratings collected in Minnesota and China were comparable. Several issues were addressed. The first issue was that of establishing a common measurement scale across groups, which involves full or partial measurement invariance of trait indicators. It is shown that using CFA or IRT models, test items that function differentially as trait indicators across groups need not interfere with comparing examinees on the same trait dimension. Second, the issue of model fit was addressed. It is proposed that person-fit statistics be used to judge the practical fit of IRT models. Finally, topics for future research are suggested.

Several studies have shown that growth hormone deficiency in adults leads to poor well-being and other clinical consequences, and that these improve when the hormone is replaced. However, the studies employed generic measures of health status that miss important aspects of the patients' experience and that have inadequate reliability and responsiveness. This paper describes the European development and testing of the Quality of Life-Assessment of Growth Hormone Deficiency in Adults (QoL-AGHDA), a condition-specific quality of life measure for use in clinical trials and for the routine monitoring of patients. The instrument was produced in five languages; English, Swedish, Italian, German and Spanish. Each language version is shown to have good reliability, internal consistency and construct validity. The QoL-AGHDA is currently included in an international database monitoring the long-term efficacy and safety of growth hormone replacement therapy and in clinical trials in a number of countries.

In this paper we outline a conception of attentional networks arising from imaging studies as connections between activated brain areas carrying out localized mental operations. We consider both the areas of functional activation (nodes) and the structural (DTI) and functional connections (DCM) between them in real time (EEG, frequency analysis) as important tools in analyzing the network. The efficiency of network function involves the time course of activation of nodes and their connectivity to other areas of the network. We outline landmarks in the development of brain networks underlying executive attention from infancy and childhood. We use individual differences in network efficiency to examine genetic alleles that are related to performance. We consider how animal studies might be used to determine the genes that influence network development. Finally we indicate how training may aid in enhancing attentional networks. Our goal is to show the wide range of methods that can be used to suggest and analyze models of network function in the study of attention.

Numerous experimental and computational approaches have been developed to predict human drug metabolism. Since databases of human drug metabolism information are widely available, these can be used to train computational algorithms and generate predictive approaches. In turn, they may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrug) and extracted a fraction of the human drug metabolism data. These data were used along with augmented atom descriptors in a predictive machine learning model, kernel-partial least squares (K-PLS). A total of 317 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites, e.g., N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, O-dealkylation, aromatic hydroxylation, aliphatic hydroxylation, O-glucuronidation, and O-sulfation gave area under the curve values from 0.75 to 0.84 and were able to predict between 61 and 79% active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules, as well as the external evaluation of novel molecules.

Previous research has not explored the Five Factor Model of personality among adults who stutter. Therefore, the present study investigated the five personality domains of Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness, as measured by the NEO Five Factor Inventory (NEO-FFI), in a sample of 93 adults seeking speech treatment for stuttering, and compared these scores with normative data from an Australian and a United States sample. Results revealed that NEO-FFI scores for the stuttering group were within the 'average' range for all five personality domains. However, adults who stutter were characterized by significantly higher Neuroticism, and significantly lower Agreeableness and Conscientiousness, than normative samples. No significant differences were found between groups on the dimensions of Extraversion and Openness. These results are discussed with reference to the relationship between personality factors among adults who stutter, their directionality, and implications for predicting treatment outcome. LEARNING OUTCOMES: The reader will be able to: (1) describe the Five Factor Model of personality, including the NEO-FFI personality domains of Extraversion, Neuroticism, Openness, Agreeableness, and Conscientiousness, and (2) discuss differences in NEO-FFI domain scores between adults who stutter and normative samples, and (3) understand the clinical implications of personality profiles in terms of treatment process and outcome for adults who stutter.

Attention-deficit hyperactivity disorder (ADHD) is described as the most common neurobehavioral condition of childhood. We raise the concern that ADHD is not a disease per se but rather a group of symptoms representing a final common behavioral pathway for a gamut of emotional, psychological, and/or learning problems. Increasing numbers of children, especially boys, are diagnosed with ADHD and treated with stimulant medications according to a simplified approach. Methodical review of the literature, however, raised concerning issues. "Core" ADHD symptoms of inattentiveness, hyperactivity and impulsivity are not unique to ADHD. Rates of "comorbid" psychiatric and learning problems, including depression and anxiety, range from 12 to 60%, with significant symptom overlap with ADHD, difficulties in diagnosis, and evidence-based treatment methods that do not include stimulant medications. No neuropsychologic test result is pathognomic for ADHD, and structural and functional neuroimaging studies have not identified a unique etiology for ADHD. No genetic marker has been consistently identified, and heritability studies are confounded by familial environmental factors. The validity of the Conners' Rating Scale-Revised has been seriously questioned, and parent and teacher "ratings" of school children are frequently discrepant, suggesting that use of subjective informant data via scale or interview does not form an objective basis for diagnosis of ADHD. Empiric diagnostic trials of stimulant medication that produce a behavioral response have been shown not to distinguish between children with and without "ADHD." In summary, the working dogma that ADHD is a disease or neurobehavioral condition does not at this time hold up to scrutiny of evidence. Thorough evaluation of symptomatic children should be individualized, and include assessment of educational, psychologic, psychiatric, and family needs.

The study of patient-reported outcomes, now common in clinical research, had its origins in social and scientific developments during the latter 20th century. Patient-reported outcomes comprise functional and health status, health-related quality of life, and quality of life. The terms overlap and are used inconsistently, and these reports of experience should be distinguished from expressions of preference regarding health states. Regulatory standards from the United States and the European Union provide some guidance regarding reporting of patient-reported outcomes. The determination that measurement of patient-reported outcomes is important depends in part on the balance between subjective and objective outcomes of the health problem under study. Instrument selection depends to a large extent on practical considerations. A number of instruments can be identified that are frequently used in particular clinical situations. The domain coverage of commonly used generic short forms varies substantially. Individualized measurement of quality of life is possible, but resource intensive. Focus groups are useful, not only for scale development but to confirm the appropriateness of existing instruments. Under classical test theory, validity and reliability are the critical characteristics of tests. Under item response theory, validity remains central, but the focus moves from the reliability of scales to the relative levels of traits in individuals and items' relative difficulty. Plans for clinical studies should include an explicit model of the relationship of patient-reported outcomes to other parameters, as well as definition of the magnitude of difference in patient-reported outcomes that will be considered important. Prevention of missing patient-reported outcome data is particularly important; to a limited extent, a variety of statistical techniques can mitigate the consequences of missing data.

The present study is the first to utilize twin modeling to examine whether parent-teacher disagreement for ADHD ratings is due to parent or teacher bias, or due to raters observing different but valid ADHD behaviors. A joint analysis was conducted with 106 twin pairs, including twins selected for ADHD and control twin pairs. Total ADHD scores were analyzed using multiple rater models that estimate genetic and environmental contributions common to both raters and unique to each rater. Results suggest that 1) disagreement in ADHD ratings is strongly due to parents and teachers observing different ADHD behaviors, some of which is valid and some of which is due to bias, and 2) parents may be more biased than teachers in their ADHD ratings.

In addition to general cognitive and motor skills, the social-pediatric screening of developmental status for school entry (SOPESS) provides subtests for assessing speech and language in a differentiated way. In a special validation study, these domains are correlated to coextensive scales of SETK 3-5. The SOPESS features high specificity and results in reliable true negative findings. In addition, a preliminary evaluation of language skills considering migrant background is given. Children with an unsatisfactory status of language competence are treated separately in the SOPESS.

This study examined the temporal sequencing of eating and anxiety disorders to delineate which anxiety disorders increase eating disorder risk and whether individuals with eating disorders are at greater risk for particular anxiety disorders. The sample was drawn from the Oregon Adolescent Depression Project. Temporal relations between specific eating and anxiety disorders were examined after controlling for relevant variables (e.g., mood disorders, other anxiety disorders) over 14years. After excluding those with anorexia nervosa (AN) in adolescence (T1), OCD was the only T1 anxiety disorder to predict AN by age 30 (T4). No T1 anxiety disorder was associated with T4 bulimia nervosa (BN). Although T1 AN did not increase risk of any T4 anxiety disorder, T1 BN appeared to increase risk for social anxiety and panic disorders. Evidence that eating disorders may have differential relations to particular anxiety disorders could inform prevention and treatment efforts.

This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Current methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as “missing data,” but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial.

Illumina produces a number of microarray-based technologies for human genotyping. An Infinium BeadChip is a two-color platform that types between 10(5) and 10(6) single nucleotide polymorphisms (SNPs) per sample. Despite being widely used, there is a shortage of open source software to process the raw intensities from this platform into genotype calls. To this end, we have developed the R/Bioconductor package crlmm for analyzing BeadChip data. After careful preprocessing, our software applies the CRLMM algorithm to produce genotype calls, confidence scores and other quality metrics at both the SNP and sample levels. We provide access to the raw summary-level intensity data, allowing users to develop their own methods for genotype calling or copy number analysis if they wish. AVAILABILITY AND IMPLEMENTATION: The crlmm Bioconductor package is available from http://www.bioconductor.org. Data packages and documentation are available from http://rafalab.jhsph.edu/software.html.

The degree to which seasonal changes affect mood, energy, sleep, appetite, food preference, or the wish to socialize with other people has been called seasonality. Seasonal affective disorder (SAD), a condition where depressions in fall and winter alternate with non-depressed periods in spring and summer, is the most marked form of seasonality. Several lines of evidence suggest that genetic factors play an important role in the etiology of seasonality and SAD. Millions of years of evolution and adaptation have optimized human biochemical and physiological systems for function and survival under equatorial environmental conditions. Modern humans began their migration out of Africa only about 150 000 years ago. Little change in our 'equatorial' systems might have been expected over this relatively short evolutionary time-span. The author suggests that a genetic susceptibility to seasonal changes in mood and behavior is a genetic predisposition to an insufficient adaptation to temperate and high latitudes.

With the variability among alcohol users in mind, Project MATCH hypothesized several treatment matching relationships based on alcohol severity and alcohol dependence, but found limited effects. However, it is possible that the existing examinations of Project MATCH data did not fully characterize the nature of severity of alcohol dependence, as these analyses have examined dependence severity as an additive symptom count similar to the diagnostic strategy represented in the DSM-IV. We examined dependence severity as a latent trait hypothesized to have a characteristic developmental progression using Item Response Theory (IRT), and examined the implications of this approach to severity scaling in the Project MATCH data. The IRT-derived empirical continuum corresponded to an earlier theoretical model of the developmental course of alcoholism, demonstrated convergent and discriminant validity, and incremented other severity markers in predicting Alcoholics Anonymous involvement, social functioning, and readiness of change. However, it did not predict treatment outcomes or other validating variables more effectively than the measures used in the original design. Furthermore, an empirical index of person fit to this continuum did not moderate trait-validator relations including treatment outcome and treatment matching effects. Overall, findings did not support the incremental utility of a latent trait representation of alcohol severity.

Given that natural selection is so powerful at optimizing complex adaptations, why does it seem unable to eliminate genes (susceptibility alleles) that predispose to common, harmful, heritable mental disorders, such as schizophrenia or bipolar disorder? We assess three leading explanations for this apparent paradox from evolutionary genetic theory: (1) ancestral neutrality (susceptibility alleles were not harmful among ancestors), (2) balancing selection (susceptibility alleles sometimes increased fitness), and (3) polygenic mutation-selection balance (mental disorders reflect the inevitable mutational load on the thousands of genes underlying human behavior). The first two explanations are commonly assumed in psychiatric genetics and Darwinian psychiatry, while mutation-selection has often been discounted. All three models can explain persistent genetic variance in some traits under some conditions, but the first two have serious problems in explaining human mental disorders. Ancestral neutrality fails to explain low mental disorder frequencies and requires implausibly small selection coefficients against mental disorders given the data on the reproductive costs and impairment of mental disorders. Balancing selection (including spatio-temporal variation in selection, heterozygote advantage, antagonistic pleiotropy, and frequency-dependent selection) tends to favor environmentally contingent adaptations (which would show no heritability) or high-frequency alleles (which psychiatric genetics would have already found). Only polygenic mutation-selection balance seems consistent with the data on mental disorder prevalence rates, fitness costs, the likely rarity of susceptibility alleles, and the increased risks of mental disorders with brain trauma, inbreeding, and paternal age. This evolutionary genetic framework for mental disorders has wide-ranging implications for psychology, psychiatry, behavior genetics, molecular genetics, and evolutionary approaches to studying human behavior.

Studies from a range of perspectives provide evidence for a relationship between creativity and the tendency to mental illness. The present study further examined this issue by administering ques- tionnaires measuring the minor features of psychosis and autism to 31 professional “artists” (visual artists and musicians) and 28 professional “scientists” (biological scientists and physical scientists/ mathematicians). The Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE), the Hypomanic Personality Scale, and the Autism-Spectrum Quotient (AQ), were administered, in addi- tion to a shortened form of the Kent-Rosanoff Word Association Scale. The results provided strong support for the connection of artistic creativity to positive schizotypy and hypomania and the ten- dency to make unusual word associations, and somewhat weaker support for the connection of sci- entific creativity to certain components of the autism spectrum.

ABSTRACT: BACKGROUND: The COSMIN checklist (COnsensus-based Standards for the selection of health status Measurement INstruments) was developed in an international Delphi study to evaluate the methodological quality of studies on measurement properties of health-related patient reported outcomes (HR-PROs). In this paper, we explain our choices for the design requirements and preferred statistical methods for which no evidence is available in the literature or on which the Delphi panel members had substantial discussion. METHODS: The issues described in this paper are a reflection of the Delphi process in which 43 panel members participated. RESULTS: The topics discussed are internal consistency (relevance for reflective and formative models, and distinction with unidimensionality), content validity (judging relevance and comprehensiveness), hypotheses testing as an aspect of construct validity (specificity of hypotheses), criterion validity (relevance for PROs), and responsiveness (concept and relation to validity, and (in) appropriate measures). CONCLUSIONS: We expect that this paper will contribute to a better understanding of the rationale behind the items, thereby enhancing the acceptance and use of the COSMIN checklist.

Occasionally, an inter-rater reliability study must be designed so that each subject is rated by fewer than all the participating raters. If there is interest in comparing the raters' mean levels of rating, and if it is desired that each mean be estimated with the same precision, then a balanced incomplete block design for the reliability study is indicated. Methods for executing the design and for analyzing the resulting data are presented, using data from an actual study for illustration.

Clinical observations and empirical studies suggest that Seasonal Affective Disorder (SAD) is related to personality. The present study estimates the genetic and environmental correlations between the Global Seasonality Score (GSS) from the Seasonal Pattern Assessment Questionnaire and personality measures, assessed using the NEO Five Factor Inventory (NEO-FFI) and the Dimensional Assessment of Personality Pathology (DAPP) in a volunteer sample of 163 monozygotic (MZ) pairs (102 female and 61 male pairs) and 134 dizygotic (DZ) pairs (70 female, 38 male and 26 opposite-sex pairs). Large genetic correlations were found between the GSS and NEO-FFI Neuroticism (0.52: 95% CI = 0.36-0.71) and DAPP-BQ Cognitive Dysregulation (0.50: 95% CI = 0.30-0.71), Affective Lability (0.49: 95% CI = 0.29-0.77), Anxiousness (0.37: 95% CI = 0.18-0.55) and Stimulus Seeking (0.45: 95% CI = 0.25-0.64) scales. The genetic correlations with the remaining scales, such as Extraversion (0.06: 95% CI = -0.16-0.26), Compulsivity (-0.09: 95% CI = -0.31-0.12) and Submissiveness (0.15: 95% CI = -0.05-0.34) were uniformly small. All environmental correlations between the GSS and personality scales were < or = 0.19. These results provide evidence that the observed correlations between these seasonality and personality dimensions are attributable to common genetic factors and that environmental influences are domain specific.

Computerized adaptive tests (CATS) have shown to be considerably more efficient than paper-and-pencil tests. This gain is realized by offering each candidate the most informative item from an available item bank on the basis of the results of items that have already been administered. The item selection methods that are used to compose an optimum test for each individual do, however, have a number of drawbacks. Though a CAT generally presents each candidate with a different test, it often occurs that some items from the item bank are administered very frequently while others are never or hardly ever used. These two problems, i.e., overexposure and underexposure of items, can be eliminated by adding further restrictions to the item selection methods. However, this exposure control will affect the efficiency of the CAT. This paper presents a solution for both problems. The functioning of these methods will be illustrated with the results of simulation research that has been carried out to develop adaptive tests.

This article describes a general item response theory model for personality items that allows the information provided by the item response times to be used to estimate the individual trait levels. The submodel describing the item response times is a modification of Thissen's log-linear model and is based on the distance-difficulty hypothesis in personality measurement. First, the procedures for fitting the model and assessing the goodness of fit are described. Second, the gain in the precision of estimating the individual trait levels when the information provided by the response times is used is assessed. Finally, all the developments in this article are illustrated by means of an empirical example.

Recently developed psychophysical techniques permit the biasing of the processing of the stimulus by early visual channels so that responses reflect characteristics of either magno- or parvocellular pathways (Pokorny & Smith, 1997). We used such techniques to test psychophysically whether the global magnocellular dysfunction reported in schizophrenia also affects early processes. Seven schizophrenic patients and 19 normal controls participated. The task was a four-alternative forced-choice luminance discrimination, using a 2 x 2 configuration of four 1-deg squares. Target luminance threshold was determined in three conditions: the stimulus, including the target, was pulsed for 17 ms (pulse paradigm); the target was presented on a steady background of four squares (steady paradigm), or the target was presented alone (no background paradigm). We replicated previous results demonstrating magnocellular and parvocellular signatures in control participants. No evidence for an early magnocellular deficit could be detected as the thresholds of all schizophrenic observers were higher both in the steady paradigm (presumed magnocellular mediation) and in the pulse paradigm (presumed parvocellular mediation). Magnocellular dysfunction, if present in schizophrenia, must concern more integrated processes, possibly at levels at which parvocellular and magnocellular paths interact.

OBJECTIVE: In cases of insufficiently controlled blood pressure, it is important for practitioners to distinguish between "nonadherence" and "nonresponse" to antihypertensive drug treatment. A reliable and valid adherence measurement based on the patient's self-report may be helpful in daily practice. STUDY DESIGN AND SETTING: In a primary care sample with 353 hypertensive patients, we applied two self-rating instruments to assess medication adherence (the "Hill-Bone Compliance to High Blood Pressure Therapy Scale" and Morisky's "Self-Reported Measure of Medication Adherence") and compared their psychometric properties. RESULTS: Both scales showed low acceptability and insufficiency to moderate internal consistency (Cronbach's alpha=0.25 and 0.73, respectively). Their convergent validity as indexed by kappa=0.39 could be judged as "fair" at best. Testing the power to predict blood pressure >140/90mmHg, both scales showed an accuracy of 57% and 62%, respectively. The positive likelihood, that is, the increase in likelihood of high blood pressure in cases of nonadherence was 1.00 and 1.32, respectively. CONCLUSION: The use of both scales cannot be recommended. They showed considerable floor effects, and their ability to identify medication adherence was inconsistent for nearly every third patient. The power of both scales to predict uncontrolled blood pressure was essentially a chance. The underlying conceptual framework of medication adherence therefore needs to be rethought.

We developed a self-administered instrument to assess health-related quality of life (HRQL) among people with schizophrenia. The S-QoL, based on Calman's approach to the subject's point of view, is a multidimensional instrument that is sensitive to change. The scale is a 41-item questionnaire with eight subscales (psychological well-being, self-esteem, family relationships, relationships with friends, resilience, physical well-being, autonomy and sentimental life) and a total score. In-depth interviews with patients determined the pertinent issues for item development. The validation study, performed with 207 patients, showed high internal consistency reliability, reproducibility and responsiveness. Construct validity was confirmed using established clinical and HRQL measures. S-QoL covers domains that differ from areas tapped in other measures, with greater responsiveness. The S-QoL is an efficient instrument for the measurement of the impact of schizophrenia on individuals' lives.

Over the past 15 years, more than 2,000 quantitative trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin-Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents.

PLS Path Modeling (PLS-PM) is classically regarded as a component-based ap- proach to Structural Equation Models and has been more recently revisited as a general frame- work for multiple table analysis. Here we propose two new modes for estimating outer weights in PLS-PM: the PLScore Mode and the PLScow Mode. Both modes involve integrating a PLS Regression as an estimation technique within the outer estimation phase of PLS-PM. However, in PLScore Mode a PLS Regression is run under the classical PLS-PM constraints of unitary variance for the latent variable scores, while in PLScow Mode the outer weights are constrained to have a unitary norm thus importing the classical normalization constraints of PLS Regres- sion. Moreover, we show how the newly proposed modes are linked to the standard Mode A and Mode B outer estimates in PLS-PM as well as to the New Mode A recently proposed in a criterion-based approach by Tenenhaus & Tenenhaus (2009).

Dyspraxia is a non verbal neuropsychological dysfunction still unrecognized but which can generate scholar learning and behavioural disabilities. We propose, at first time, to do a state of art with the various terminologies and typologies which lead to put together clumsiness, motor coordination disorder and the different types of dyspraxia. Then, we will bring an integrative model and clinical data in children with developmental dyspraxia, allowing a better pointing, to make a diagnostic and then we suggest some advices for remediations.

BACKGROUND: Self-rated health has been shown to be a significant predictor of mortality. However, there is limited knowledge on what factors contribute to the global perception of self-rated health in patients with chronic obstructive pulmonary disease (COPD). OBJECTIVE: To describe the associations between physical and psychological symptoms, physical and mental health functioning, and perceptions of mastery with concurrent and longitudinal global self-rated health (GSRH) in patients with COPD and to determine if gender modifies these relationships. DESIGN: Cross-sectional analysis of data from a longitudinal clinical trial. SETTING: University medical center in the United States. PARTICIPANTS: 115 patients with moderate to severe COPD. METHODS: GSRH was measured using one question from the Medical Outcomes Study, SF-36 which states, "In general, would you say your health is: excellent, very good, good, fair, or poor". Physical and psychological symptoms were measured with the Shortness of Breath Questionnaire, Chronic Respiratory Questionnaire (CRQ), and Center for Epidemiologic Studies Depression Scale (CESD); the SF-36 was used to measure physical and mental health functioning; mastery was measured by a sub-scale of the CRQ. The BODE index, a multidimensional disease severity grading system, was also included. Stepwise logistic regression analyses were performed. RESULTS: In cross-sectional analyses, only disease severity as measured by the BODE index was associated with GSRH [odds ratio, 1.52; 95% confidence interval, CI (1.08, 2.15)]. Stratified analyses by gender showed that the association between the BODE index and the GSRH held up for men, but not for women. Higher perception of symptom control was associated with positive health ratings in women. Subjects with less fatigue at baseline had a lower risk of reporting poor health 12 months later [OR 0.84; 95% CI (0.72, 0.98)]. CONCLUSIONS: For patients with COPD, ratings of global health were mostly influenced by measures that reflect their physical state, e.g. disease severity and fatigue. While additional work is needed to better understand gender differences in factors that contribute to GSRH, therapeutic nursing interventions might place greater focus on symptom management if the goal is to improve patients' perceptions of their global health.

Addresses issues related to partial measurement invariance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses" to establish stable and substantively well-fitting baseline models, (b) determin- ing partially invariant measurement parameters, and (c) testing for the invariance of factor covari- ance and mean structures, given partial measurement invariance. Wealso show, explicitly, the trans- formation of parameters from an all-^fto an all-y model specification, for purposes of testing mean structures. These procedures are illustrated with multidimensional self-concept data from low (« = 248) and high (n = 582) academically tracked high school adolescents.

This article deals with the problem of interpreting health-related quality of life (HRQL) outcomes in clinical trials. First, we will briefly describe how dichotomization and item response theory can facilitate interpretation. Based on examples from the medical literature for the interpretation of HRQL scores we will show that dichotomies may help clinicians understand information provided by HRQL instruments in RCTs. They can choose thresholds to calculate proportions of patients benefiting based on absolute scores or change scores. For example, clinicians interpreting clinical trial results could consider the difference in the proportion of patients who achieve a mean score of 50 before and after an intervention on a scale from 1 to 100. For the change score approach, they could consider the proportion of patients who have changed by a score of 5 or more. Finally, they can calculate the proportion of patients benefiting and transform these numbers into a number needed to treat or natural frequencies. Second, we will describe in more detail an approach to the interpretation of HRQL scores based on the minimal important difference (MID) and proportions. The MID is the smallest difference in score in the outcome of interest that informed patients or informed proxies perceive as important, either beneficial or harmful, and that would lead the patient or clinician to consider a change in the management. Any change in management will depend on the downsides, including cost and inconvenience, associated with the intervention. Investigators can help with the interpretation of HRQL scores by determining the MID of an HRQL instrument and provide mean differences in relation to the MID. For instance, for an MID of 0.5 on a seven point scale investigators could provide the mean change on the instrument as well as the proportion of patients with scores greater than the MID. Thus, there are several steps investigators can take to facilitate this process to help bringing HRQL information closer to the bedside.

The impetus of psychological research is the inability of psychologists to accommodate new phenomena or problems with their existing knowledge. Conducting research is a formal and systematic exercise for the following reasons. First, conceptual skills are deployed to propose a theory for the to-be-explained phenomenon. Second, deductive logic is used to derive the research hypotheses from the theory. This is possible only if the theory is sufficiently specific. Third, researchers collect data systematically according to a plan or design. Fourth, the inductive rule that underlies the experimental design makes it possible to exclude some potential interpretations of the data. Fifth, appropriate statistical procedures are used to tabulate and analyze the data. Lastly, deductive logic is used to draw the theoretical conclusion. In short, the success of the research process depends on a confluence of conceptual, meta-theoretical, methodological, and statistical skills. For various reasons, psychologists may emphasize some of the six aforementioned reasons at the expense of the other issues. Consequently, psychologists use a wide array of research methods. This sometimes gives the impression of fundamental methodological differences among psychologists. While this is not necessarily undesirable, it is hoped that the discussion of the meta-theoretical and philosophical issues serves to set the methodological disagreements among psychologists in the proper context. For example, before considering whether empirical research should be driven atheoretically by data or be guided conceptually by theory, it may be helpful to examine first whether or not there is “pure” observation. At the same time, realizing that all observations are theory-dependent, should we conclude that no objectivity is possible, particularly when psychologists appeal to the incorporeal entity, the mind? Before attempting to answer the question as to whether or not the mind can be reduced to the brain, we may find it necessary to see how cognitive psychologists study unobservable hypothetical structures or processes like perception, memory, intelligence, motives, and the like. Explanations are qualitative in the sense that psychological phenomena are explained in terms of hypothetical mechanisms to which theoretical properties are attributed. Are psychologists being inconsistent when they insist on using statistics or psychometric tests? How is it possible to use 1 quantitative data as evidential support for qualitative theories? How do psychologists generalize from their data that are collected in an artificial setting to real-life phenomena? What is the rationale of experimentation in psychological research? How can psychologists assess their research?

ABSTRACT Objective: To identify the key methodological issues in the construction of population-level EuroQol 5-dimensions (EQ-5D)/time trade-off (TTO) preference elicitation studies. Method: This study involved three components. The first was to identify existing population-level EQ-5D TTO studies. The second was to illustrate and discuss the key areas of divergence between studies, including the international comparison of tariffs. The third was to portray the relative merits of each of the approaches and to compare the results of studies across countries. Results: While most articles report use of the protocol developed in the original UK study, we identified three key areas of divergence in the construction and analysis of surveys. These are the number of health states valued to determine the algorithm for estimating all health states, the approach to valuing states worse than immediate death, and the choice of algorithm. The evidence on international comparisons suggests differences between countries although it is difficult to disentangle differences in cultural attitudes with random error and differences as a result of methodological divergence. Conclusions: Differences in methods may obscure true differences in values between countries. Nevertheless, population-specific valuation sets for countries engaging in economic evaluation would better reflect cultural differences and are therefore more likely to accurately represent societal attitudes.

OBJECTIVES: Motor vehicle crashes are a leading cause of mortality and morbidity worldwide. Even though trauma centers provide the gold standard of care for motor vehicle crash patients with life- or limb-threatening injuries, many whose lives might be saved by trauma center care are treated instead at non-trauma center hospitals. Triage algorithms, designed to identify patients with life- or limb-threatening injuries who should be transported to a trauma center, lack appropriate sensitivity to many of these injuries. The challenge to the trauma community is differentiating patients with life- or limb-threatening injuries from those with less severe injuries at the crash scene so that the patients can be transported to the most appropriate level of care. The purpose of this study was to use crash scene data available to emergency responders to classify adults with moderate and severe injuries. These classifiers might be useful to guide triage decision making. METHODS AND MATERIAL: Records of 74,626 adults, age 18-64 years, from the National Automotive Sampling System Crashworthiness Data Systems database were analyzed using classification and regression trees (CART) analysis. Both CART models (moderate injury and severe injury) included 13 predictor variables. The response variables were the targeted injury severity score cut points for moderate and severe injury. Two final classification trees were developed: one that classified occupants based on moderate injury and the other on severe injury. Misclassification costs were manipulated to achieve the best model fit for each tree. RESULTS: The moderate injury classification tree had three splitters: police-estimated injury severity, restraint use, and number of persons injured. The severe injury classification tree had four splitters: police-estimated injury severity, manner of collision, number of persons injured in the crash, and age. Sensitivity and specificity of the classification trees were 93.70%, 77.53% (moderate) and 99.18%, 73.96% (severe), respectively. CONCLUSIONS: CART analysis can be used to classify injury severity using crash scene information that is available to emergency responders. This procedure offers an opportunity to examine alternative methods of identifying injury severity that might assist emergency responders to differentiate more accurately persons who should receive trauma center care from those who can be treated safely at a non-trauma center hospital.

AIM: To combine and test the EORTC questionnaires for assessing quality of life (HRQL) for oesophageal (QLQ-OES18) and stomach cancer (QLQ-STO22), into a single questionnaire for tumours of the oesophagus, oesophago-gastric junction or stomach. METHODS: The QLQ-OES18, QLQ-STO22 and seven modified items were administered to 300 patients with oesophageal (n=148), junctional (n=66), or gastric cancer (n=86). Semi-structured interviews assessed item and scale preference and multi-trait scaling analyses confirmed the scale structure of the new module (QLQ-OG25). This was further tested for validity. RESULTS: The QLQ-OG25 has six scales, dysphagia, eating restrictions, reflux, odynophagia, pain and anxiety. Scales have good reliability (alpha range 0.67-0.87) and they distinguish between tumour sites and disease stage. Scales do not correlate highly with scores from the core questionnaire, thus indicating that the module was addressing separate HRQL aspects. CONCLUSION: The QLQ-OG25 is recommended to supplement the EORTC QLQ-C30 when assessing HRQL in patients with oesophageal, junctional or gastric cancer.

Dystrobrevin binding protein 1 (DTNBP1) has been identified as putative schizophrenia susceptibility gene, but it remains unknown whether polymorphisms relate to altered cerebral structure. We examined relationships between a previously implicated DTNBP1 risk variant [P1578] and global and segmented brain tissue volumes and regional cortical thickness in schizophrenia (n = 62; 24 risk carriers) and healthy subjects (n = 42; 11 risk carriers), across ethnic groups and within Caucasians. Schizophrenia patients showed similar brain volumes, but significantly reduced brain-size adjusted gray matter and CSF volumes and cortical thinning in a widespread neocortical distribution compared to controls. DTNBP1 risk was found associated with reduced brain volume, but not with tissue sub-compartments. Cortical thickness, which was weakly associated with brain size, showed regional variations in association with genetic risk, although effects were dominated by highly significant genotype by diagnosis interactions over broad areas of cortex. Risk status was found associated with regional cortical thinning in patients, particularly in temporal networks, but with thickness increases in controls. DTNBP1 effects for brain volume and cortical thickness appear driven by different neurobiological processes. Smaller brain volumes observed in risk carriers may relate to previously reported DTNBP1/cognitive function relationships irrespective of diagnosis. Regional cortical thinning in patient, but not in control risk carriers, may suggest that DTNBP1 interacts with other schizophrenia-related risk factors to affect laminar thickness. Alternatively, DTNBP1 may influence neural processes for which individuals with thicker cortex are less vulnerable. Although DTNBP1 relates to cortical thinning in schizophrenia, morphological changes in the disorder are influenced by additional genetic and/or environmental factors.

Covariance selection seeks to estimate a covariance matrix by maximum likelihood while restricting the number of nonzero inverse covariance matrix coefficients. A single penalty pa- rameter usually controls the tradeoff between log likelihood and sparsity in the inverse matrix. We describe an efficient algorithm for computing a full regularization path of solutions to this problem.

Although several studies have assessed the relationships between the temperament dimensions of the Cloninger model of personality and depression, little is known about the role played by the character dimensions proposed by the seven-factor model of Cloninger in depression. In this study, the relationships between the Temperament and Character Inventory (TCI) and depression were examined in a sample of 40 major depressive patients and 40 healthy controls. Depressed patients exhibit higher harm avoidance and self-transcendence scores as well as lower self-directedness and cooperativeness scores as compared to healthy controls. However, the three other dimensions do not differ between depressive patients and controls. Among the depressive group, harm avoidance, self-directedness and cooperativeness dimensions are related to the severity of depression as assessed by the Hamilton scale. This study confirms the state dependence of the harm avoidance dimension and suggests a relationship between the character dimensions of the Cloninger model and depression.

Ordinal scale response items are often used in quantifying a latent trait. The mode in which these items are administered may effect an item's characteristics, such as the item's location on the latent scale and the efficiency of the item in discriminating between different values of the latent trait. We present the Bayesian Differential Mode Effects Model (BDMEM), a Bayesian Item Response Theory (IRT) model for the detection and quantification of mode of administration effects at both the item and form level. To illustrate the BDMEM, we present an example of a mental health survey administered both by telephone and self-administered questionnaire. The BDMEM is compared to the popular approach of IRT differ- ential item functioning (DIF) evaluation, and its advantages over DIF are highlighted.

This paper illustrates the use of an explanatory item response modeling (EIRM) approach in the context of measuring group differences in science achievement. The distinction between item response models and EIRMs, recently elaborated by De Boeck & Wilson (2004), is presented within the statistical framework of generalized linear mixed models. It is shown that the EIRM approach provides a powerful framework for both a psychometric and statistical analysis of group differences. This is contrasted with the more typical two-step approach, in which psychometric analysis (i.e., measurement) and statistical analysis (i.e., explanation) occur independently. The two approaches are each used to describe and explain racial/ethnic gaps on a standardized science test. It is shown that the EIRM approach results in estimated racial/ethnic achievement gaps that are larger than those found in the two-step approach. In addition, when science achievement is examined by subdomains, the magnitude of racial/ethnic gap estimates under the EIRM approach are more variable and sensitive to the inclusion of contextual variables. These differences stem from the fact that the EIRM approach allows for disattenuated estimates of group level parameters, while the two-step approach depends upon estimates of science achievement that are shrunken as a function of measurement error.

BACKGROUND: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. RESULTS: We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. CONCLUSION: Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.

A model-based procedure for assessing the extent to which missing data can be ignored and handling non-ignorable missing data is presented. The procedure is based on item response theory modelling. As an example, the approach is worked out in detail in conjunction with item response data modelled using the partial credit and generalized partial credit models. Simulation studies are carried out to assess the extent to which the bias caused by ignoring the missing-data mechanism can be reduced. Finally, the feasibility of the procedure is demonstrated using data from a study to calibrate a medical disability scale.

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises commonfactor and structural regression models. The introduction of covariance and meanstructure models reduces the numberof parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidenceintervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.

Twin studies have demonstrated that personality traits show moderate genetic influence. The conclusions drawn from twin studies rely on the assumptions that twins are representative of the population at large and that monozygotic and dizygotic twins are comparable in every way that might have bearing on the traits being studied. To evaluate these assumptions, we used Multidimensional Personality Questionnaire (MPQ) data from three samples drawn from the Minnesota Twin Registry (totaling 12,971 respondents) to examine the effect sizes associated with mean differences on the 11 MPQ scales and 3 higher-order MPQ factors for singletons versus twins and MZ twins versus DZ twins. The singletons in the samples were family members of the participating twins. We also used ratios of scale variances to examine the significance of variance differences. The only mean or variance difference replicated across all three samples was greater Social Closeness (about.1 standard deviation) for twins than for singletons. This difference was obtained for both males and females. It would appear that, with respect to personality, twins are not systematically different from other people. Our results also highlight the importance of replication in psychological research because each of our large samples showed differences not replicated in other samples.

Assessments for quality of life (QOL) of the adolescent have received relatively little attention in the literature. Although there is no consensus on the definition of adolescent QOL and what aspects should be measured, it is generally accepted that QOL is a multidimensional construct. The objective of this study is to determine the measurement properties of the latent factors underlying adolescent QOL based on a second-order confirmatory factor analysis (CFA). A recursive structural equation model (SEM) is then proposed to determine the direction and magnitude of the interdependent effects among the latent factors. The questionnaire used was the Quality of Life Profile-Adolescent Version (QOLPAV). A sample of 363 adolescents was recruited from 20 secondary schools in Perth, Australia. The second-order CFA suggested that adolescent QOL may be measured by five underlying constructs namely social, environment, psychological, health, and opportunities for growth. The interdependent relations among these constructs identified the environment factor as primary, exerting both direct and indirect effects on the other four factors.

This paper provides an alternative methodology for the analysis of a set of Likert responses measured on a common attitudinal scale when the primary focus of interest is on the relative importance of items in the set. The method makes fewer assumptions about the distribution of the responses than the more usual approaches such as comparisons of means, MANOVA or ordinal data methods. The approach transforms the Likert responses into paired comparison responses between the items. The complete multivariate pattern of responses thus produced can be analysed by an appropriately reformulated paired comparison model. The dependency structure between item responses can also be modelled flexibly. The advan- tage of this approach is that sets of Likert responses can be analysed simultaneously within the Generalized Linear Model framework, providing standard likelihood based inference for model selection. This method is applied to a recent international survey on the importance of environmental problems.

We describe a novel approach for evaluating SNP genotypes of a genome-wide association scan to identify "ethnic outlier" subjects whose ethnicity is different or admixed compared to most other subjects in the genotyped sample set. Each ethnic outlier is detected by counting a genomic excess of "rare" heterozygotes and/or homozygotes whose frequencies are low (<1%) within genotypes of the sample set being evaluated. This method also enables simple and striking visualization of non-Caucasian chromosomal DNA segments interspersed within the chromosomes of ethnically admixed individuals. We show that this visualization of the mosaic structure of admixed human chromosomes gives results similar to another visualization method (SABER) but with much less computational time and burden. We also show that other methods for detecting ethnic outliers are enhanced by evaluating only genomic regions of visualized admixture rather than diluting outlier ancestry by evaluating the entire genome considered in aggregate. We have validated our method in the Wellcome Trust Case Control Consortium (WTCCC) study of 17,000 subjects as well as in HapMap subjects and simulated outliers of known ethnicity and admixture. The method's ability to precisely delineate chromosomal segments of non-Caucasian ethnicity has enabled us to demonstrate previously unreported non-Caucasian admixture in two HapMap Caucasian parents and in a number of WTCCC subjects. Its sensitive detection of ethnic outliers and simple visual discrimination of discrete chromosomal segments of different ethnicity implies that this method of rare heterozygotes and homozygotes (RHH) is likely to have diverse and important applications in humans and other species.

Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Mircorarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs)simultaneously. The starting point for the statistical analyses used by GWAS, to determine association between loci and disease, are genotype calls (AA, AB, or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various so- phisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays, and different sample batches has substantial in- uence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability, GWAS run the risk of adversely affect- ing the quality of reported findings. In this paper we present solutions based on a multi-level mixed model. Software implementation of the method described in this paper is available as free and open source code in the crlmm R/BioConductor.

Hierarchical cluster analysis is shown to be an effective method for forming scales from sets of items. Comparisons with factor analytic techniques suggest that hierarchical analysis is superior in some respects for scale construction

OBJECTIVES: The objective of this literature review is to provide an overview of the evidence for pediatric patient self-report in pediatric oncology. Methods A review of the general literature on pediatric health-related quality of life (HRQOL) measurement as background, with pediatric patient self-report data from the Journal of Pediatric Psychology during the past 5 years in pediatric oncology summarized. Utilizing the PedsQL available at (http://www.pedsql.org), data are presented to illustrate child and parent reports in pediatric oncology. Results Data demonstrate that children as young as 5 years of age can reliably and validly self-report their HRQOL when an age-appropriate instrument is utilized. Conclusions The evidence supports including pediatric patients' perspectives in clinical trials. Parent proxy-report is recommended when pediatric patients are too young, too cognitively impaired, too ill or fatigued to complete a HRQOL instrument, but not as a substitute for child self-report when the child is willing and able to provide their perspective.

Longitudinal clinical trials in psychiatry have used various statistical methods to examine treatment effects. The validity of the inferences depends upon the different method's assumptions and whether a given study violates those assumptions. The objective of this paper was to elucidate these complex issues by comparing various methods for handling missing data (e.g., last observation carried forward [LOCF], completer analysis, propensity-adjusted multiple imputation) and for analyzing outcome (e.g., end-point analysis, repeated-measures analysis of variance [RM-ANOVA], mixed-effects models [MEMs]) using data from a multi-site randomized controlled trial in obsessive-compulsive disorder (OCD). The trial compared the effects of 12 weeks of exposure and ritual prevention (EX/RP), clomipramine (CMI), their combination (EX/RP&CMI) or pill placebo in 122 adults with OCD. The primary outcome measure was the Yale-Brown Obsessive Compulsive Scale. For most comparisons, inferences about the relative efficacy of the different treatments were impervious to different methods for handling missing data and analyzing outcome. However, when EX/RP was compared to CMI and when CMI was compared to placebo, traditional methods (e.g., LOCF, RM-ANOVA) led to different inferences than currently recommended alternatives (e.g., multiple imputation based on estimation-maximization algorithm, MEMs). Thus, inferences about treatment efficacy can be affected by statistical choices. This is most likely when there are small but potentially clinically meaningful treatment differences and when sample sizes are modest. The use of appropriate statistical methods in psychiatric trials can advance public health by ensuring that valid inferences are made about treatment efficacy.

A higher incidence of major depression has been described in adults with a primary oxidative phosphorylation disease. Intriguingly however, not all patients carrying the same mutation develop symptoms of major depression, pointing out the significance of the interplay of genetic and non-genetic factors in the etiology. In a series of paediatric patients evaluated for mitochondrial dysfunction, out of 35 children with a biochemically and genetically confirmed mitochondrial disorder, we identified five cases presenting with major depression prior to the diagnosis. The patients were diagnosed respectively with mutations in MTTK, MTND1, POLG1, PDHA1 and the common 4977 bp mtDNA deletion. Besides cerebral lactic acidemia protein and glucose concentrations, immunoglobins, anti-gangliosides and neurotransmitters were normal. No significant difference could be confirmed in the disease progression or the quality of life, compared to the other, genetically confirmed mitochondrial patients. In three out of our five patients a significant stress life event was confirmed. We propose the abnormal central nervous system energy metabolism as the underlying cause of the mood disorder in our paediatric patients. Exploring the genetic etiology in children with mitochondrial dysfunction and depression is essential both for safe medication and adequate counselling.

ABSTRACT : Cardiovascular diseases are associated with combinations of phenotypic traits, which are in turn caused by a combination of environmental and genetic factors. Because of the diversity of pathways that may lead to cardiovascular diseases, we examined the so-called intermediate phenotypes, which are often repeatedly measured. We developed a penalized nonlinear canonical correlation analysis to associate multiple repeatedly measured traits with high-dimensional single-nucleotide polymorphism data.

Although initially believed to contain orthogonal dimensions, the Big Five personality taxonomy appears to have a replicable higher-order structure, with the metatrait of Plasticity reflecting the shared variance between Extraversion and Openness/Intellect, and the metatrait of Stability reflecting the shared variance among Neuroticism, Agreeableness, and Conscientiousness. These higher order traits have been theorized to relate to individual differences in the functioning of the dopamine and serotonin systems, respectively. As dopamine is associated with exploration and incentive-related action, and serotonin with satiety and constraint, this neuropharmacological trait theory has behavioral implications, which we tested in 307 adults by examining the association of a large number of behavioral acts with multi-informant reports of the metatraits. The frequencies of acts were consistently positively correlated with Plasticity and negatively correlated with Stability. At the broadest level of description, variation in human personality appears to reflect engagement and restraint of behavior.

OBJECTIVES: Recently, an increasing number of systematic reviews have been published in which the measurement properties of health status questionnaires are compared. For a meaningful comparison, quality criteria for measurement properties are needed. Our aim was to develop quality criteria for design, methods, and outcomes of studies on the development and evaluation of health status questionnaires. STUDY DESIGN AND SETTING: Quality criteria for content validity, internal consistency, criterion validity, construct validity, reproducibility, longitudinal validity, responsiveness, floor and ceiling effects, and interpretability were derived from existing guidelines and consensus within our research group. RESULTS: For each measurement property a criterion was defined for a positive, negative, or indeterminate rating, depending on the design, methods, and outcomes of the validation study. CONCLUSION: Our criteria make a substantial contribution toward defining explicit quality criteria for measurement properties of health status questionnaires. Our criteria can be used in systematic reviews of health status questionnaires, to detect shortcomings and gaps in knowledge of measurement properties, and to design validation studies. The future challenge will be to refine and complete the criteria and to reach broad consensus, especially on quality criteria for good measurement properties.

The etiology of schizophrenia likely involves genetic interactions. DISC1, a promising candidate susceptibility gene, encodes a protein which interacts with many other proteins, including CIT, NDEL1, NDE1, FEZ1 and PAFAH1B1, some of which also have been associated with psychosis. We tested for epistasis between these genes in a schizophrenia case-control study using machine learning algorithms (MLAs: random forest, generalized boosted regression and Monte Carlo logic regression). Convergence of MLAs revealed a subset of seven SNPs that were subjected to 2-SNP interaction modeling using likelihood ratio tests for nested unconditional logistic regression models. Of the (7)C(2) = 21 interactions, four were significant at the alpha = 0.05 level: DISC1 rs1411771-CIT rs10744743 OR = 3.07 (1.37, 6.98) p = 0.007; CIT rs3847960-CIT rs203332 OR = 2.90 (1.45, 5.79) p = 0.003; CIT rs3847960-CIT rs440299 OR = 2.16 (1.04, 4.46) p = 0.038; one survived Bonferroni correction (NDEL1 rs4791707-CIT rs10744743 OR = 4.44 (2.22, 8.88) p = 0.00013). Three of four interactions were validated via functional magnetic resonance imaging (fMRI) in an independent sample of healthy controls; risk associated alleles at both SNPs predicted prefrontal cortical inefficiency during the N-back task, a schizophrenia-linked intermediate biological phenotype: rs3847960-rs440299; rs1411771-rs10744743, rs4791707-rs10744743 (SPM5 p < 0.05, corrected), although we were unable to statistically replicate the interactions in other clinical samples. Interestingly, the CIT SNPs are proximal to exons that encode the DISC1 interaction domain. In addition, the 3' UTR DISC1 rs1411771 is predicted to be an exonic splicing enhancer and the NDEL1 SNP is  3,000 bp from the exon encoding the region of NDEL1 that interacts with the DISC1 protein, giving a plausible biological basis for epistasis signals validated by fMRI.

The linguistic abilities of children born preterm at 32 weeks' gestation or earlier at Kuopio University Hospital during 1984 to 1986 were evaluated during successive phases of a prospective study. The study protocol included the Rapid Automatic Naming test and Wechsler Intelligence Scale for Children - Revised at 9 years of age and a modified Stroop Color-Word test and the Wechsler Intelligence Scale - Revised at the age of 16 years. Fifty-one children born preterm (26 males, 25 females) and 51 age-matched and sex-matched term controls (26 males, 25 females) were studied at the age of 9 years. At the age of 16 years, 40 children born preterm (19 males, 21 females) and 31 term controls (14 males, 17 females) participated in the study. The children born preterm scored significantly lower in two naming tasks than the controls at the age of 9 years. However, there was no difference between the study groups in naming skills at the age of 16 years or in verbal IQ in either study phase. Maternal education level was not associated with naming skills. Thus, the consequences of preterm birth seem to be minor in relation to linguistic skills during school age and diminish by adolescence.

BACKGROUND AND AIMS: Two widely used evaluation tools for the quality of life are the 36-item Short-Form Health Survey (SF-36) and World Health Organization Quality of Life Assessment (100-item version) (WHOQOL-100), however, these tools have not been compared for patients with stroke to date. The specific objectives of this study were: 1) to study the effect of stroke on quality of life (QOL) as measured by the SF-36 and by the WHOQOL-100, and 2) to compare these two instruments. SETTINGS AND DESIGN: Seventy patients who were admitted to the neurology clinic six months after stroke were included in this study. PATIENTS AND METHODS: As a data-collecting device, the SF-36 and WHOQOL-100 scales were used. An additional questionnaire was administered to obtain demographic data. STATISTICAL ANALYSIS: Pearson correlation analysis was performed and Blant-Altman Plots were used. Psychometric analysis was performed. RESULTS: In stroke, the most flustered domains of quality of life were vitality and general health perception fields in the SF-36 and in the WHOQL-100, independence level field, overall QOL and general health perceptions. While there was a fair degree of relationship (r= 0.25-0.50) between general health perceptions, physical, social and mental fields that were similar fields of scales, a fair and moderate to good relationship was found between different fields. Limits of agreement in similar domains of the two instruments were very large. In all four demonstrated Bland-Altman plots, there was agreement of the scales in the measurements of similar fields of quality of life. CONCLUSION: This study demonstrated that both the SF-36 and WHOQOL-100 quality of life scales are useful in the practical evaluation of patients with stroke.

Aim: To determine minimally important differences (MIDs) for scales in the first version of the Copenhagen Psychosocial Questionnaire (COPSOQ). Methods: Data were taken from two separate studies: a national population survey (N 1⁄4 1062), and an intervention study at 14 workplaces (N 1⁄4 1505). On the basis of the population survey, the MID for each COPSOQ scale was calculated as one-half of a standard deviation (0.5 SD). For the core COPSOQ scales on “Quantitative demands”, “Influence at work”, “Predictability”, “Social support (from colleagues and supervisors, respectively)”, and “Job satisfaction”, the MIDs were evaluated in the intervention study, where score differences for the scales were linked to the respondents' global self-evaluation of the impact of the interventions. The scales were scored from 0 to 100 in both studies. Results: The MIDs calculated as 0.5 SD were, on average, 9.2 (range 6.8-14.9) for the long version scales, and 10.8 (range 7.6-14.9) for the medium-length version scales. The analysis of the self-evaluated changes on the scale scores for the core COPSOQ scales showed that the anchor-based estimates of MID were generally lower than 0.5 SD. Conclusions: We recommend the following MID values for the COPSOQ scales: “Quantitative demands”, 0.3 SD; “Influence”, 0.2 SD; “Predictability”, 0.3 SD; “Social support from colleagues”, 0.3 SD; “Social support from supervisor”, 0.7 SD; and “Job satisfaction”, 0.4 SD. For all other COPSOQ scales, where we do not have anchor-based results, we recommend the conventional MID value of 0.5 SD.

Cortical GABA deficits that are consistently reported in schizophrenia may reflect an etiology of failed normal postnatal neurotransmitter maturation. Previous studies have found prefrontal cortical GABAA receptor a subunit alterations in schizophrenia, yet their relationship to normal developmental expres- sion profiles in the human cortex has not been determined. The aim of this study was to quantify GABAA receptor a-subunit mRNA expression patterns in human dorsolateral prefrontal cortex (DLPFC) during normal postnatal development and in schizophrenia cases compared to controls. Transcript levels of GABAA receptor a subunits were measured using microarray and qPCR analysis of 60 normal individuals aged 6 weeks to 49 years and in 37 patients with schizophrenia/schizoaffective disorder and 37 matched controls. We detected robust opposing changes in cortical GABAA receptor a1 and a5 subunits during the first few years of postnatal development, with a 60% decrease in a5 mRNA expression and a doubling of a1 mRNA expression with increasing age. In our Australian schizophrenia cohort we detected decreased GAD67 mRNA expression (p = 0.0012) and decreased a5 mRNA expression (p = 0.038) in the DLPFC with no significant change of other a subunits. Our findings confirm that GABA deficits (reduced GAD67) are a consistent feature of schizophrenia postmortem brain studies. Our study does not confirm alterations in cortical a1 or a2 mRNA levels in the schizophrenic DLPFC, as seen in previous studies, but instead we report a novel down-regulation of a5 subunit mRNA suggesting that post-synaptic alterations of inhibi- tory receptors are an important feature of schizophrenia but may vary between cohorts.

The coefficients of individual agreement (CIAs), which are based on the ratio of the intra- and inter-observer disagreement, provide a general approach for evaluating agreement between two fixed methods of measurements or human observers. In this paper, programs in both SAS and R are presented for estimation of the CIAs between two observers with quantitative or binary measurements. A detailed illustration of the computations, macro variable definitions, input and output for the SAS and R programs are also included in the text. The programs provide estimations of CIAs, their standard errors as well as confidence intervals, for the cases with or without a reference method. Data from a carotid stenosis screening study is used as an example of quantitative measurements. Data from a study involving the evaluation of mammograms by ten radiologists is used to illustrate a binary data example.

Using reliable outcome measures is a necessity for the occupational therapy profession in enabling valid assessments of clients. Although Cronbach's alpha is the most widely applied index of internal consistency reliability, there are misconceptions about its use and interpretation. This paper aims to guide assessment developers in paediatric occupational therapy, as well as practitioners who are evaluating outcome measures in using and interpreting the Cronbach's alpha estimates appropriately. This will enable them to decide on the tools' clinical value and incorporate them into their practice with children. Method:  Previously published papers reporting on internal consistency issues of outcome measures in paediatric occupational therapy were searched through the Allied and Complementary Medicine database. These papers were used as a basis to discuss possible reasons for reporting of low internal consistency. Results:  The analysis demonstrates that Cronbach's alpha reports are not always interpreted in a sound way. The paper emphasises that one should be cautious about judging estimates of internal consistency. Low size of the coefficient alpha might not always indicate problems with the construction of the tool; whereas large sizes do not always suggest adequate reliability. Instead, these reports might be related to the data characteristics of the construct. Conclusion:  In judging an outcome measure's internal consistency, researchers and practitioners in occupational therapy should report and consider the nature of data, the scale's length and width, the linearity and the normality of response distribution, the central response tendency, the sample response variability and the sample size.

The goal of the present study is to examine genetic and environmental influences on maternal and teacher ratings of Attention Problems (AP) in 7-year-old children. Teachers completed the Teacher Report Form (N=2259 pairs), and mothers the Child Behavior Checklist (N=2057 pairs). Higher correlations were found in twins rated by the same teacher than in twins rated by different teachers. This can be explained by rater bias or by a greater environmental sharing in twins, who are in the same classroom. We further found that 41% of the variation in maternal and teacher ratings is explained by a common factor. The heritability of this common factor is 78%. The heritabilities of the rater specific factors of mothers and teachers are 76% and 39%, respectively. Because Attention Problems that are persistent over situations may indicate more serious behavior problems than context dependent Attention Problems, we believe that gene finding strategies should focus on this common phenotype.

OBJECTIVE: We conducted a critical literature review of studies assessing the prevalence of mood disorders (MD) in subjects with eating disorders (ED; anorexia nervosa and bulimia nervosa). In the first part of this article, we discuss methodological issues relevant to comorbidity studies between ED and MD. In the second part, we summarize the findings of these studies in light of the methodological considerations raised. METHOD: A manual computerised search (Medline) was performed for all published studies on comorbidity between ED and MD. In order to have sufficiently homogeneous diagnostic criteria for both categories of disorders, this search was limited to articles published between 1985 and 2006. RESULTS: Too few studies include control groups, few studies compared diagnostic subgroups of ED subjects, and results are scarce or conflicting. DISCUSSION: The results are discussed in the light of the methodological problems observed. The implications when reviewing the results of published studies and planning future research are set out.

A putatively functional tetranucleotide repeat polymorphism in the tyrosine hydroxylase gene (TH) has been investigated with regard to different aspects of psychopathology. We investigated whether reported associations of this TH polymorphism may reflect associations with common personality traits. Personality was assessed by the NEO Personality Inventory-Revised version (NEO PI-R), in 205 healthy Caucasian volunteers. Tendencies for higher scores in the neuroticism (N) facets, Angry hostility (P=0.008) and Vulnerability (P=0.021), were observed among carriers of one of the alleles (T8). Healthy women with the T6/T10 genotype had significantly higher scores (P=0.001) in the Deliberation and Dutifulness facets (P=0.031) (the Conscientiousness dimension, C) and lower scores (P=0.031) in the Feelings facet (the Openness dimension, O). We concluded that: (1) higher mean scores in the Neuroticism facets among T8 allele carriers are consistent with previous data and warrants further research; (2) the T6/T10 genotype may influence personality among women; (3) these data should be cautiously interpreted in the absence of corroborating data.

It has been suggested that the quality of clinical trials should be assessed by blinded raters to limit the risk of introducing bias into meta-analyses and systematic reviews, and into the peer-review process. There is very little evidence in the literature to substantiate this. This study describes the development of an instrument to assess the quality of reports of randomized clinical trials (RCTs) in pain research and its use to determine the effect of rater blinding on the assessments of quality. A multidisciplinary panel of six judges produced an initial version of the instrument. Fourteen raters from three different backgrounds assessed the quality of 36 research reports in pain research, selected from three different samples. Seven were allocated randomly to perform the assessments under blind conditions. The final version of the instrument included three items. These items were scored consistently by all the raters regardless of background and could discriminate between reports from the different samples. Blind assessments produced significantly lower and more consistent scores than open assessments. The implications of this finding for systematic reviews, meta-analytic research and the peer-review process are discussed.

Variation in gene expression is an important mechanism underlying susceptibility to complex disease. The simultaneous genome-wide assay of gene expression and genetic variation allows the mapping of the genetic factors that underpin individual differences in quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically generated eQTL information could provide immediate insight into a biological basis for disease associations identified through genome-wide association (GWA) studies, and can help to identify networks of genes involved in disease pathogenesis. Although there are limitations to current eQTL maps, understanding of disease will be enhanced with novel technologies and international efforts that extend to a wide range of new samples and tissues.

Genome-wide patterns of variation across individuals provide a powerful source of data for uncovering the history of migration, range expansion, and adaptation of the human species. However, high-resolution surveys of variation in genotype, haplotype and copy number have generally focused on a small number of population groups. Here we report the analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms (SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations. Analysis of SNP genotypes yields strongly supported fine-scale inferences about population structure. Increasing linkage disequilibrium is observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. New approaches for haplotype analysis produce inferences about population structure that complement results based on unphased SNPs. Despite a difference from SNPs in the frequency spectrum of the copy-number variants (CNVs) detected-including a comparatively large number of CNVs in previously unexamined populations from Oceania and the Americas-the global distribution of CNVs largely accords with population structure analyses for SNP data sets of similar size. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations.

The seven-factor model of personality developed by Cloninger and colleagues describes personality as a function of developmental aspects of character superimposed on heritable dimensions of temperament. The objective of this study was to determine whether this model could be applied to early childhood. We tested a preschool version of the Temperament and Character Inventory (the preschool TCI) in 305 children aged 2-5 years. Exploratory factor analysis provided support for the presence of distinct domains of temperament (comprising four factors) and character (comprising three factors). The preschool TCI demonstrated high internal consistency for each of the seven factors (Cronbach's alpha values: 0.70-0.93). Inter-individual differences in novelty seeking, reward dependence and cooperativeness were highly preserved (Pearson's r values 0.75, 0.64 and 0.80, respectively) in 29 subjects who were studied over a 3-year period from toddlerhood to early school age. Future studies are warranted to test the extent to which early childhood measurements of the seven factors might predict the development of personality disorders.

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays. R and packages from the Bioconductor project are used as an analytical engine in combination with the R function Sweave, which allows automatic generation of analysis reports. These report files contain all R commands used to perform the analysis and guarantee therefore a maximum transparency and reproducibility for each analysis. The web application is implemented in Java based on the latest J2EE (Java 2 Enterprise Edition) software technology. CARMAweb is freely available at https://carmaweb.genome.tugraz.at.

Health-related knowledge is an important component in the self-management of chronic illnesses. The objective of this study was to more accurately assess racial differences in hypertension knowledge by using a latent variable modeling approach that controlled for sociodemographic factors and accounted for measurement issues in the assessment of hypertension knowledge. Cross-sectional data from 1,177 participants (45% African American; 35% female) were analyzed using a multiple indicator multiple causes (MIMIC) modeling approach. Available sociodemographic data included race, education, sex, financial status, and age. All participants completed six items on a hypertension knowledge questionnaire. Overall, the final model suggested that females, Whites, and patients with at least a high school diploma had higher latent knowledge scores than males, African Americans, and patients with less than a high school diploma, respectively. The model also detected differential item functioning (DIF) based on race for two of the items. Specifically, the error rate for African Americans was lower than would be expected given the lower level of latent knowledge on the items, on the questions related to: (a) the association between high blood pressure and kidney disease, and (b) the increased risk African Americans have for developing hypertension. Not accounting for DIF resulted in the difference between Whites and African Americans to be underestimated. These results are discussed in the context of the need for careful measurement of health-related constructs, and how measurement-related issues can result in an inaccurate estimation of racial differences in hypertension knowledge.

BACKGROUND AND OBJECTIVES: Evaluate a patient-reported outcomes questionnaire that uses computerized adaptive testing (CAT) to measure the impact of osteoarthritis (OA) on functioning and well-being. MATERIALS AND METHODS: OA patients completed 37 questions about the impact of OA on physical, social and role functioning, emotional well-being, and vitality. Questionnaire responses were calibrated and scored using item response theory, and two scores were estimated: a Total-OA score based on patients' responses to all 37 questions, and a simulated CAT-OA score where the computer selected and scored the five most informative questions for each patient. Agreement between Total-OA and CAT-OA scores was assessed using correlations. Discriminant validity of Total-OA and CAT-OA scores was assessed with analysis of variance. Criterion measures included OA pain and severity, patient global assessment, and missed work days. RESULTS: Simulated CAT-OA and Total-OA scores correlated highly (r = 0.96). Both Total-OA and simulated CAT-OA scores discriminated significantly between patients differing on the criterion measures. F-statistics across criterion measures ranged from 39.0 (P < .001) to 225.1 (P < .001) for the Total-OA score, and from 40.5 (P < .001) to 221.5 (P < .001) for the simulated CAT-OA score. CONCLUSIONS: CAT methods produce valid and precise estimates of the impact of OA on functioning and well-being with significant reduction in response burden.

BACKGROUND: An unresolved issue in psychiatry research concerns the assumption that detrimental effects of negative life events on mental health may be buffered by a multitude of positive life events. However, there is clear lack of empirical evidence for this assumption, and one may even argue that positive life events act as additional stressors and thus increase (and not decrease) the risk for affective disorders. METHODS: Data were used from 4796 adults aged 18-64, who participated in 2 waves (i.e., 1997 and 1999) of NEMESIS, a prospective-epidemiological study. Measures were based on diagnoses of DSM-III-R mood disorders, and a life events questionnaire employed in the NEMESIS study. RESULTS: Although the prevalence of mood disorders correlated positively with both the number of negative and positive life events experienced, a multivariate path analysis (Mplus) demonstrated that only negative life events longitudinally predicted mood disorders. Positive life events predicted subsequent mood disorders only when in the same time period a high number of negative events were experienced. CONCLUSIONS: Positive life events do not buffer the detrimental impact of negative ones, but instead may function as additional stressor, in the context of highly erratic life course patterns that may be typical for depressed individuals.

Factor analysis has been one of the most powerful and flexible tools for assessment of multivariate dependence and codependence. Loosely speaking, it could be argued that the origin of its success rests in its very exploratory nature, where various kinds of data-relationships amongst the variables at study can be iteratively verified and/or refuted. Bayesian inference in factor analytic models has received renewed attention in recent years, partly due to computational advances but also partly to applied focuses generating factor structures as exemplified by recent work in financial time series modeling. The focus of our current work is on exploring questions of uncertainty about the number of latent factors in a multi- variate factor model, combined with methodological and computational issues of model specification and model fitting. We explore reversible jump MCMC methods that build on sets of parallel Gibbs sampling-based analyses to generate suitable empirical proposal distributions and that address the challenging problem of finding efficient proposals in high-dimensional models. Alternative MCMC methods based on bridge sampling are discussed, and these fully Bayesian MCMC approaches are compared with a collection of popular model selection methods in empirical stud- ies. Various additional computational issues are discussed, including situations where prior information is scarce, and the methods are explored in studies of some simulated data sets and an econometric time series example.

This cross-sectional work studies the prevalence of post-partum blues on days 3 and 5 after delivery and the links between post-partum blues and depressive symptomatology, using standardised interviews and rating scales (Kennerley and Gath Blues Scale. MADRS) to screen a consecutive series of 104 women on days three and five after a normal delivery. This study stresses the possibility of a difference between the symptomatology of a benign "classical" post-partum blues, and that of a more intense blues closer to the spectrum of depressive mood disorders and perhaps post-natal depression.

BACKGROUND: Gene x environment models are widely used to assess genetic and environmental risks and their association with a phenotype of interest for many complex diseases. Mixed generalized linear models were used to assess gene x environment interactions with respect to systolic blood pressure on sibships adjusting for repeated measures and hierarchical nesting structures. A data set containing 410 sibships from the Framingham Heart Study offspring cohort (part of the Genetic Analysis Workshop 13 data) was used for all analyses. Three mixed gene x environment models, all adjusting for repeated measurement and varying levels of nesting, were compared for precision of estimates: 1) all sibships with adjustment for two levels of nesting (sibs within sibships and sibs within pedigrees), 2) all sibships with adjustment for one level of nesting (sibs within sibships), and 3) 100 data sets containing random draws of one sibship per extended pedigree adjusting for one level of nesting. RESULTS: The main effects were: gender, baseline age, body mass index (BMI), hypertensive treatment, cigarettes per day, grams of alcohol per day, and marker GATA48G07A. The interaction fixed effects were: baseline age by gender, baseline age by cigarettes per day, baseline age by hypertensive treatment, baseline age by BMI, hypertensive treatment by BMI, and baseline age by marker GATA48G07A. The estimates for all three nesting techniques were not widely discrepant, but precision of estimates and determination of significant effects did change with the change in adjustment for nesting. CONCLUSION: Our results show the importance of the adjustment for all levels of hierarchical nesting of sibs in the presence of repeated measures.

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ∗ ∗ ∗, 148-156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

OBJECTIVE: To evaluate the ability of health-related quality of life (HRQOL) measures to detect change over time in persons with epilepsy. BACKGROUND: The application of HRQOL measures in clinical trials has been limited by a dearth of information regarding their abilities to measure change over time (i.e., their responsiveness). To calculate responsiveness, one must categorize subjects as "changed" or "unchanged" by a priori criteria. METHODS: The authors analyzed data collected at baseline and at 28-week follow-up from an antiepileptic drug trial. Two different criteria for classifying subjects as changed or unchanged-one based on seizure frequency (where changed = attainment of seizure freedom) and one based on self-reported overall condition (where changed = improvement in overall condition)-were used. We compared responsiveness indices for two generic (Short Form [SF]-36 and SF-12) and two epilepsy-targeted (Quality of Life in Epilepsy [QOLIE]-89 and QOLIE-31) HRQOL measures. Two scoring procedures for the SF-36, one based on classic test theory and the other on item response theory (IRT), were compared. RESULTS: Effect sizes of the most responsive HRQOL measures were medium to large. The shorter epilepsy-targeted measure had similar responsiveness indices to those of the longer measure. Epilepsy-targeted measures were consistently more responsive than generic measures under the overall condition criterion, but for the seizure freedom criterion, IRT scoring of the SF-36 yielded responsiveness indices comparable to those of the epilepsy-targeted measures. CONCLUSION: Epilepsy-targeted health-related quality of life measures may be preferable to generic ones in longitudinal studies. Selection of a shorter epilepsy-targeted measure does not compromise responsiveness. Item response theory scoring should be applied to epilepsy-targeted HRQOL measures.

ABSTRACT : Rheumatoid arthritis (RA) is three times more common in females than in males, suggesting that sex may play a role in modifying genetic associations with disease. We have addressed this hypothesis by performing sex-differentiated and sex-interaction analyses of a genome-wide association study of RA in a North American population. Our results identify a number of novel associations that demonstrate strong evidence of association in both sexes combined, with no evidence of heterogeneity in risk between males and females. However, our analyses also highlight a number of associations with RA in males or females only. These signals may represent true sex-specific effects, or may reflect a lack of power to detect association in the smaller sample of males, and thus warrant further investigation.

Racial health inequality is related to socioeconomic status (SES), but debate ensues on the nature of the relationship. Using the US National Health and Nutrition Examination Survey I and the subsequent follow-up interviews, this research examines health disparities between white and black adults and whether the SES/health gradient differs across the two groups in the USA. Two competing mechanisms for the conditional or interactive relationship between race and SES on health are examined during a 20-year period for black and white Americans. Results show that black adults began the study with more serious illnesses and poorer self-rated health than white adults and that the disparity continued over the 20 years. Significant interactions were found between race and education as well as race and employment status on health outcomes. The interaction effect of race and education showed that the racial disparity in self-rated health was largest at the higher levels of SES, providing some evidence for the "diminishing returns" hypothesis; as education levels increased, black adults did not have the same improvement in self-rated health as white adults. Overall, the findings provide evidence for the continuing significance of both race and SES in determining health status over time.

Finding multilevel association rules in transaction databases is most commonly seen in is widely used in data mining. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed fuzzy set concepts, multi-level taxonomy and different minimum supports to find fuzzy multilevel association rules in a given transaction data set. Apriori property is used in model to prune the item sets. The proposed model adopts a top- down progressively deepening approach to derive large itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary intervals. An example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.

BACKGROUND: The published clinical research literature may be distorted by the pursuit of statistically significant results. PURPOSE: We aimed to develop a test to explore biases stemming from the pursuit of nominal statistical significance. METHODS: The exploratory test evaluates whether there is a relative excess of formally significant findings in the published literature due to any reason (e.g., publication bias, selective analyses and outcome reporting, or fabricated data). The number of expected studies with statistically significant results is estimated and compared against the number of observed significant studies. The main application uses alpha = 0.05, but a range of alpha thresholds is also examined. Different values or prior distributions of the effect size are assumed. Given the typically low power (few studies per research question), the test may be best applied across domains of many meta-analyses that share common characteristics (interventions, outcomes, study populations, research environment). RESULTS: We evaluated illustratively eight meta-analyses of clinical trials with >50 studies each and 10 meta-analyses of clinical efficacy for neuroleptic agents in schizophrenia; the 10 meta-analyses were also examined as a composite domain. Different results were obtained against commonly used tests of publication bias. We demonstrated a clear or possible excess of significant studies in 6 of 8 large meta-analyses and in the wide domain of neuroleptic treatments. LIMITATIONS: The proposed test is exploratory, may depend on prior assumptions, and should be applied cautiously. CONCLUSIONS: An excess of significant findings may be documented in some clinical research fields.

We discuss interactive techniques for multidimensional scaling (MDS) and a two sys- tems, named “GGvis” and “XGvis”, that implement these techniques. MDS is a method for visualizing proximity data, that is, data where objects are char- acterized by dissimilarity values for all pairs of objects. MDS constructs maps (called “configurations”) of these objects in IRk by interpreting the dissimilarities as distances. As a data-mapping technique, MDS is fundamentally a visualization method. It is hence plausible that MDS gains in power if it is embedded in a data visualization environment. Consequently, the MDS systems presented here are conceived as exten- sions of multivariate data visualization systems (“GGvis” and “X/GGobi” in this case). The visual analysis of MDS output profits from dynamic projection tools for viewing high-dimensional configurations, from brushing multiple linked views, from plot en- hancements such as labels, glyphs, colors, lines, and from selective removal of groups of objects. Powerful is also the ability to move points and groups of points interactively and thereby create new starting configurations for MDS optimization. In addition to the benefits of a data visualization environment, we enhance MDS by providing interactive control over numerous options and parameters, a few of them novel. They include choices of 1) metric versus nonmetric MDS, 2) classical versus dis- tance MDS, 3) the configuration dimension, 4) power transformations for metric MDS, 5) distance transformations and 6) Minkowski metrics for distance MDS, 7) weights in the form of powers of dissimilarities and 8) as a function of group memberships, 9) var- ious types of group-dependent MDS such as multidimensional unfolding and external unfolding, 10) random subselection of dissimilarities, 11) perturbation of configura- tions, and 12) a separate window for diagnostics, including the Shepard plot. MDS was originally developed for the social sciences, but it is now also used for laying out graphs. Graph layout is usually done in 2-D, but we allow layouts in arbitrary dimensions. We show applications to the mapping of computer usage data, to the dimension reduction of marketing segmentation data, to the layout of mathematical graphs and social networks, and finally to the spatial reconstruction of molecules.

Anorexia nervosa is a perplexing illness marked by low body weight and persistent fear of weight gain. Anorexia nervosa has the highest mortality rate of any psychiatric disease. Historically, anorexia nervosa was viewed as a disorder primarily influenced by sociocultural factors; however, over the past decade, this perception has been challenged. Family studies have consistently demonstrated that anorexia nervosa runs in families. Twin studies have underscored the contribution of additive genetic factors to the observed familial aggregation. With these bodies of literature as a starting point, we evaluate critically the current state of research on molecular genetic studies of anorexia nervosa and provide guidance for future research.

BACKGROUND: Comparisons of structured diagnostic interviews with clinical assessments in general population samples show marked discrepancies. In order to validate the CIS-R, a fully structured diagnostic interview used for the National Survey of Psychiatric Morbidity in Great Britain, it was compared with SCAN, a standard, semi-structured, clinical assessment. METHODS: A random sample of 1882 Leicestershire addresses from the Postcode Address File yielded 1157 eligible adults: of these 860 completed the CIS-R; 387 adults scores > or = 8 on the CIS-R and 205 of these completed a SCAN reference examination. Neurotic symptoms, in the previous week and month only, were enquired about. Concordance was estimated for ICD-10 neurotic and depressive disorders, F32 to F42 and for depression symptom score. RESULTS: Sociodemographic characteristics closely resembled National Survey and 1991 census profiles. Concordance was poor for any ICD-10 neurotic disorder (kappa = 0.25 (95% CI, 0.1-0.4)) and for depressive disorder (kappa = 0.23 (95% CI, 0-0.46)). Sensitivity to the SCAN reference classification was also poor. Specificity ranged from 0.8 to 0.9. Rank order correlation for total depression symptoms was 0.43 (Kendall's tau b; P < 0.001; N = 205). DISCUSSION: High specificity indicates that the CIS-R and SCAN agree that prevalence rates for specific disorders are low compared with estimates in some community surveys. We have revealed substantial discrepancies in case finding. Therefore, published data on service utilization designed to estimate unmet need in populations requires re-interpretation. The value of large-scale CIS-R survey data can be enhanced considerably by the incorporation of concurrent semi-structured clinical assessments.

The present study examined how social attention is influenced by social content and the presence of items that are available for attention. We monitored observers' eye movements while they freely viewed real-world social scenes containing either 1 or 3 people situated among a variety of objects. Building from the work of Yarbus (1965/1967) we hypothesized that observers would demonstrate a preferential bias to fixate the eyes of the people in the scene, although other items would also receive attention. In addition, we hypothesized that fixations to the eyes would increase as the social content (i.e., number of people) increased. Both hypotheses were supported by the data, and we also found that the level of activity in the scene influenced attention to eyes when social content was high. The present results provide support for the notion that the eyes are selected by others in order to extract social information. Our study also suggests a simple and surreptitious methodology for studying social attention to real-world stimuli in a range of populations, such as those with autism spectrum disorders.

AIMS: To investigate the effects of small for gestational age (SGA) in very low birthweight (VLBW) infants on growth and development until the fifth year of life. METHODS: VLBW (< 1500 g) infants, selected from a prospective study, were classified as SGA (n = 115) on the basis of birth weight below the 10th percentile for gestational age and were compared with two groups of appropriate for gestational age (AGA) infants matched according to birth weight (AGA-BW; n = 115) or gestation at birth (AGA-GA; n = 115). Prenatal, perinatal, and postnatal risk factors were recorded, and duration and intensity of treatment were computed from daily assessments. Body weight, length, and head circumference were measured at birth, five and 20 months (corrected for prematurity), and at 56 months. General development was assessed at five and 20 months with the Griffiths scale of babies abilities, and cognitive development at 56 months with the Columbia mental maturity scales, a vocabulary (AWST) and language comprehension test (LSVTA). RESULTS: Significant group differences were found in complications (pregnancy, birth, and neonatal), parity, and multiple birth rate. The AGA-GA group showed most satisfactory growth up to 56 months, with both the AGA-BW and SGA groups lagging behind. The AGA-GA group also scored significantly more highly on all developmental and cognitive tests than the other groups. Developmental test results were similar for the SGA and AGA-BW groups at five and 20 months, but AGA-BW infants (lowest gestation) had lower scores on performance intelligence quotient and language comprehension at 56 months than the SGA group. When prenatal and neonatal complications, parity, and multiple birth were accounted for, group differences in growth remained, but differences in cognitive outcome disappeared after five months. CONCLUSIONS: Being underweight and with a short gestation (SGA and VLBW) leads to poor weight gain and head growth in infancy but does not result in poorer growth than in infants of the same birth weight but shorter gestation (AGA-BW) in the long term. SGA is related to early developmental delay and later language problems; however, neonatal complications may have a larger detrimental effect on long term cognitive development of VLBW infants than whether they are born SGA or AGA.

The linear logistic test model (LLTM) specifies the item parameters as a weighted sum of basic parameters. The LLTM is a special case of a more general non-linear logistic test model (NLTM) where the weights are partially unknown. This paper is about the identifiability of the NLTM. Sufficient and necessary conditions for global identifiability are presented for a NLTM where the weights are linear functions, while conditions for local identifiability are shown to require less assumptions. It is also discussed how these conditions are checked using an algorithm due to Bekker, Merckens, and Wansbeek (1994). Several illustrations are given.

BACKGROUND: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. RESULTS: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. CONCLUSION: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Clinical trial investigators often record a great deal of baseline data on each patient at randomization. When reporting the trial's findings such baseline data can be used for (i) subgroup analyses which explore whether there is evidence that the treatment difference depends on certain patient characteristics, (ii) covariate-adjusted analyses which aim to refine the analysis of the overall treatment difference by taking account of the fact that some baseline characteristics are related to outcome and may be unbalanced between treatment groups, and (iii) baseline comparisons which compare the baseline characteristics of patients in each treatment group for any possible (unlucky) differences. This paper examines how these issues are currently tackled in the medical journals, based on a recent survey of 50 trial reports in four major journals. The statistical ramifications are explored, major problems are highlighted and recommendations for future practice are proposed. Key issues include: the overuse and overinterpretation of subgroup analyses; the underuse of appropriate statistical tests for interaction; inconsistencies in the use of covariate-adjustment; the lack of clear guidelines on covariate selection; the overuse of baseline comparisons in some studies; the misuses of significance tests for baseline comparability, and the need for trials to have a predefined statistical analysis plan for all these uses of baseline data.

Recent work has focused on explicating the relations among the current mood and anxiety disorders. This research has yielded some important findings (e.g., the very strong link between generalized anxiety disorder and the unipolar mood disorders). I discuss problems associated with disorder-based analyses, however, and I argue that they need to be supplemented by examining relations among the specific symptom dimensions within these diagnostic classes. I demonstrate that two quantitative elements need to be considered when analyzing the properties of symptoms-the level of specificity and the magnitude of the general distress variance. These quantitative elements can be used to organize relevant symptoms into four groups (i.e., a quadripartite model) that reflect varying combinations of distress and specificity. I illustrate the value of this approach by reviewing the properties of the major symptom dimensions within posttraumatic stress disorder, obsessive-compulsive disorder, and major depression.

Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs.

The inability to quantify the risk for disorders, such as substance use disorders (SUD), hinders etiology research and development of targeted intervention. Based on the concept of common transmissible liability to SUD related to illicit drugs, a method enabling quantification of this latent trait has been developed, utilizing high-risk design and item response theory. This study examined properties of a SUD transmissible liability index (TLI) derived using this method. Sons of males with or without SUD were studied longitudinally from preadolescence to young adulthood. The properties of TLI, including its psychometric characteristics, longitudinal risk assessment and ethnic variation, were examined. A pilot twin study was conducted to analyze the composition of TLI's phenotypic variance. The data suggest that TLI has concurrent, incremental, predictive and discriminant validity, as well as ethnic differences. The data suggest a high heritability of the index in males. The results suggest applicability of the method for genetic and other etiology-related research, and for evaluation of individual risk.

Postoperative Atrial Fibrillation (PoAF) is the most common arrhythmia after heart surgery, and continues to be a major cause of morbidity. Due to the complexity of this condition, many genes and/or environmental factors may play a role in susceptibility. Previous findings have shown several clinical and genetic risk factors for the development of PoAF. The goal of this study was to determine whether interactions among candidate genes and a variety of clinical factors are associated with PoAF. We applied the Multifactor Dimensionality Reduction (MDR) method to detect interactions in a sample of 940 adult subjects undergoing elective procedures of the heart or great vessels, requiring general anesthesia and sternotomy or thoracotomy, where 255 developed PoAF. We took a random sample of controls matched to the 255 AF cases for a total sample size of 510 individuals. MDR is a powerful statistical approach used to detect gene-gene or gene-environment interactions in the presence or absence of statistically detectable main effects in pharmacogenomics studies. We chose polymorphisms in three (IL-6, ACE, and ApoE) candidate genes, all previously implicated in PoAF risk, and a variety of environmental factors for analysis. We detected a single locus effect of IL-6 which is able to correctly predict disease status with 58.8% (p<0.001) accuracy. We also detected an interaction between history of AF and length of hospital stay that predicted disease status with 68.34% (p<0.001) accuracy. These findings demonstrate the utility of novel computational approaches for the detection of disease susceptibility genes. While each of these results looks interesting, they only explain part of PoAF susceptibility. It will be important to collect a larger set of candidate genes and environmental factors to better characterize the development of PoAF. Applying this approach, we were able to elucidate potential associations with postoperative atrial fibrillation.

Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or 'phenotype' into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed models (categorical phenotypes). We illustrate the proposed approach by modeling family data on the continuous phenotype birth weight and twin data on the dichotomous phenotype depression. The example data sets and commands for Stata and R/S-PLUS are available at the Biometrics website.

The aim of our study was to determine the impact of psychiatric comorbidities on the health-related quality of life of HCV-infected patients. Assessment of clinical, socio-demographic and quality of life data of the patients followed up at a Hepatology unit was performed by using a standard questionnaire and the SF-36 instrument. Psychiatric diagnoses were confirmed by using the Mini International Neuropsychiatric Interview, Brazilian version 5.0.0 (MINI Plus). Evaluation using the MINI plus demonstrated that 46 (51%) patients did not have any psychiatric diagnosis, while 44 (49%) had at least one psychiatric diagnosis. Among patients with a psychiatric comorbidity, 26 (59.1%) had a current mental disorder, out of which 22 (84.6%) had not been previously diagnosed. Patients with psychiatric disorders had lower scores in all dimensions of the SF-36 when compared to those who had no psychiatric diagnosis. Scores of physical functioning and bodily pain domains were lower for those suffering from a current psychiatric disorder when compared to those who had had a psychiatric disorder in the past. Females had lower scores of bodily pain and mental health dimensions when compared to males. Scores for mental health dimension were also lower for patients with advanced fibrosis. The presence of a psychiatric comorbidity was the variable that was most associated with the different scores in the SF-36, compared to other variables such as age, gender, aminotransferase levels, and degree of fibrosis.

Aim It has been suggested that one approach to identifying motor impairment in children is to use the Child Behavior Checklist (CBCL) as a screening tool. The current study examined the validity of the CBCL in identifying motor impairment. Method A total of 398 children, 206 females and 192 males, aged from 3 years 9 months to 14 years 10 months were assessed on the McCarron Assessment of Neuromuscular Development to determine their motor ability. Parents completed the CBCL. Results The 'Clumsy' item on the CBCL was found to predict motor ability independent of the child's age, sex, and scores on other items of the CBCL. However, the sensitivity of the 'Clumsy' item in terms of identifying motor impairment was found to be a low 16.7% compared with specificity of 93.2%. The item 'Not liked' was also found to be a significant predictor of motor impairment. Interpretation Although the 'Clumsy' and 'Not liked' items were found to have a relationship with motor ability, they should not be relied upon to categorize children as motor impaired versus not impaired. It is possible that these items may be better indicators of motor impairment in children with developmental disorders such as attention-deficit-hyperactivity disorder, but clinical samples are needed to address this.

This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy, such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays. Effect displays are constructed by identifying high-order terms in a generalised linear model. Fitted values under the model are computed for each such term. The lower-order `relatives' of a high-order term (e.g., main effects marginal to an interaction) are absorbed into the term, allowing the predictors appearing in the high-order term to range over their values. The values of other predictors are fixed at typical values: for example, a covariate could be fixed at its mean or median, a factor at its proportional distribution in the data, or to equal proportions in its several levels. Variations of effect displays are also described, including representation of terms higher-order to any appearing in the model.

This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.

Diagnostic validity of the DAST was assessed using a clinical sample of 501 drug/alcohol patients. Various DAST cut-points were validated against DSM-III drug abuse/dependence criteria, as assessed by the Diagnostic Interview Schedule. The DAST attained 85% overall accuracy in classifying patients according to DSM-III diagnosis. This accuracy was maintained between DAST score cut-points of 5/6 through 9/10. Receiver Operating Characteristic analysis indicated that 5/6 was the optimum threshold score. The DAST was also correlated with demographic variables, psychiatric history, and drug use. The results showed very good concurrent and discriminant validity. This study concluded that fairly accurate estimation of DSM-III drug criteria could be made using a brief self-administered questionnaire (DAST). However, caution must be expressed when generalizing these findings to other contexts (e.g. the justice system) where subjects may have stronger motivation to under-report drug involvement.

The graphical presentation of any scientific finding enhances its description, in- terpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixtures of linear structural equations. Second, it provides several examples showing how the method works and how it is implemented and interpreted in practical applications. In particular, this article examines the potentially nonlinear relationships between positive and negative affect and cognitive processing. Third, a recommended dis- play format for illustrating latent bivariate relationships is demonstrated. Finally, the article describes an R package and an online utility that generate these displays automatically.

To date, many studies have been conducted to compare the performance of different DIF procedures using simulated data sets. However, some results from these simulation studies are inconsistent with one other (e.g., Hidalgo & López-Pina, 2004; Jodoin & Gierl, 2001). This study used real data to systematically investigate the consistencies of DIF detection and effect size among three widely-used DIF procedures: Mantel-Haenszel (MH), Simultaneous Item Bias Test (SIBTEST), and logistic regression (LR). Several indicators, including correlations among DIF procedures effect size measures, matching percentages, and relative matching percentages, were used to evaluate the consistencies among the DIF procedures. The results showed high correlations among DIF effect size measures, moderate to high matching percentages among DIF classifications, and a broad range of relative matching percentages among DIF procedures.

Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Ap- proaches such as exponential family PCA and non-negative matrix factorisation have successfully extended PCA to non-Gaussian data types, but these techniques fail to take advantage of Bayesian inference and can suffer from problems of over- fitting and poor generalisation. This paper presents a fully probabilistic approach to PCA, which is generalised to the exponential family, based on Hybrid Monte Carlo sampling. We describe the model which is based on a factorisation of the observed data matrix, and show performance of the model on both synthetic and real data.

We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise. Our approach consists in reframing this task in a variable selection con- text. We use a penalized least-squares criterion with a l1-type penalty for this purpose. We prove some theoretical results on the estimated change-points and on the underlying piecewise constant estimated function. Then, we explain how to implement this method in practice by combining the LAR algorithm and a re- duced version of the dynamic programming algorithm and we apply it to synthetic and real data.

OBJECTIVE: This study evaluated a model of the impact of borderline and antisocial personality disorder indications on HIV symptoms and health-related quality of life (HRQoL) in AIDS-bereaved adults, accounting for grief severity, social support, and years since HIV diagnosis. DESIGN: Structural equation modeling was used to test the proposed model in a sample of 268 HIV-seropositive adults enrolled in an intervention for coping with AIDS-related bereavement. MAIN OUTCOME MEASURES: Functional assessment of HIV infection, HIV symptoms. RESULTS: The proposed model demonstrated excellent fit with study data and all hypothesized paths were supported. Personality disorder indication was directly related to HIV symptoms and HRQoL and indirectly related through both social support and grief severity. Social support was negatively related to HIV symptoms and positively related to HRQoL, while grief severity was positively related to HIV symptoms and negatively related to HRQoL. Finally, HIV symptoms had a direct negative relationship with HRQoL. CONCLUSION: Personality disorders have a direct negative effect on HIV symptoms and HRQoL and indirect effects through grief severity and social support.

Depression is composed of multiple subcomponents including social, cognitive, affective, and somatic symptomatology. Many widely used self-report scales are also multidimensional, suggesting that the subcomponents of depression are empirically differentiated, yet the use of a composite score is the common practice. The authors argue that a closer examination of the subscales of symptom inventories, and their interrelationships, can further our understanding of the development and persistence of depression. Structural equation modeling is used on the French version of CES-D responses (Radloff, 1977) from 1,734 participants, providing an explicit test of causal relations between several response classes commonly associated with depression. These structural models are consistent with a view of depression as a process that unfolds over time and are tested within both a cross-sectional and a prospective framework. They are compared to a higher-order factor model which speaks to the nature, but not the development, of depression.

Analysing and clustering units described by a mixture of sets of quantitative, categorical and frequency variables is a relevant challenge. Multiple factor analysis is extended to include these three types of variables in order to balance the influence of the different sets when a global distance between units is computed. Suitable coding is adopted to keep as close as possible to the approach offered by principal axes methods, that is, principal component analysis for quantitative sets, multiple correspondence analysis for categorical sets and correspondence analysis for frequency sets. In addition, the presence of frequency sets poses the problem of selecting the unit weighting, since this is fixed by the user (usually uniform) in principal component analysis and multiple correspondence analysis, but imposed by the table margin in correspondence analysis. The method's main steps are presented and illustrated by an example extracted from a survey that aimed to cluster respondents to a questionnaire that included both closed and open-ended questions.

A problem in ethnic minority mental health that can be solved in the foreseeable future is understanding how subtle and covert forms of racism affect psychological health of racial minorities. Although scientific psychology has generated a large body of literature on racial prejudice, stereotypes, intergroup attitudes, and racial bias and their often implicit and automatic nature, relatively little is known about the effects of these subtle racial bias on minority indi- viduals. Following a selective review of recent developments in experimental psychology and multicultural psychology, I suggest some promising approaches and opportunities for future integration that would advance the field.

Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines)- e.g., as in bar plots, pie charts, mosaic displays or heatmaps-it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Here, we discuss how the perceptually-based Hue-Chroma-Luminance (HCL) color space can be used for deriving suitable color palettes for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).

Six measures of physiological dysregulation were derived from 11 clinically assessed biomarkers, and related to health outcomes and health behaviors for the Hawaii Personality and Health cohort (N = 470). Measures summing extreme scores at one tail of the biomarker distributions performed better than ones summing both tails, and continuous measures performed better than count scores. Health behaviors predicted men's dysregulation but not women's. Dysregulation and health behaviors predicted self-rated health for both men and women, and depressive symptoms predicted self-rated health only for women. These findings provide preliminary guidelines for constructing valid summary measures of global health status for use in health psychology.

The Committee on Medical Products for Human Use (CHMP) has recently issued two new guidance documents that are of particular note to statisticians. The purpose of this short note is to give a little background to the origins of these documents and a flavour of some of their most important features. The two guidelines are reproduced as the next two papers in the journal.

According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that the traditional maximum likelihood ratio statistic, TML, overestimates nominal Type I error rates up to 70% under conditions of multivariate normality. Some alternative statistics for the correction of model-size effects were also investigated: the scaled Satorra-Bentler statistic, TS C ; the adjusted Satorra- Bentler statistic, TAD (Satorra & Bentler, 1988, 1994); corresponding Bartlett corrections, TMLb, TSCb, and TADb (Bartlett, 1950); and corresponding Swain corrections, TMLs, TSCs, and TADs (Swain, 1975). The empirical findings in- dicate that the model test statistic TMLs should be applied when large structural equation models are analyzed and the observed variables have (approximately) a multivariate normal distribution.

BACKGROUND: Most clinical trial publications include figures, but there is little guidance on what results should be displayed as figures and how. PURPOSE: To evaluate the current use of figures in Trial reports, and to make constructive suggestions for future practice. METHODS: We surveyed all 77 reports of randomised controlled trials in five general medical journals during November 2006 to January 2007. The numbers and types of figures were determined, and then each Figure was assessed for its style, content, clarity and suitability. As a consequence, guidelines are developed for presenting figures, both in general and for each specific common type of Figure. RESULTS: Most trial reports contained one to three figures, mean 2.3 per article. The four main types were flow diagram, Kaplan Meier plot, Forest plot (for subgroup analyses) and repeated measures over time: these accounted for 92% of all figures published. For each type of figure there is a considerable diversity of practice in both style and content which we illustrate with selected examples of both good and bad practice. Some pointers on what to do, and what to avoid, are derived from our critical evaluation of these articles' use of figures. CONCLUSION: There is considerable scope for authors to improve their use of figures in clinical trial reports, as regards which figures to choose, their style of presentation and labelling, and their specific content. Particular improvements are needed for the four main types of figures commonly used.

In a survey of eighty-eight county districts within England and Wales, rates of Alzheimer's disease in people under the age of 70 years were estimated from the records of the computerised tomographic (CT) scanning units that served these districts. Rates were adjusted to compensate for differences in distance from the nearest CT scanning unit and for differences in the size of the population served by the units. Aluminium concentrations in water over the past 10 years were obtained from water authorities and water companies. The risk of Alzheimer's disease was 1.5 times higher in districts where the mean aluminium concentration exceeded 0.11 mg/l than in districts where concentrations were less than 0.01 mg/l. There was no evidence of a relation between other causes of dementia, or epilepsy, and aluminium concentrations in water.

That a test not be biased is an important consideration in the selection and use of any psychological test. That is, it is essential that a test is fair to all applicants, and is not biased against a segment of the applicant population. Bias can result in systematic errors that distort the inferences made in selection and classification. In many cases, test items are biased due to the fact that they contain sources of difficulty that are irrelevant or extraneous to the construct being measured, and these extraneous or irrelevant factors affect performance. Perhaps the item is tapping a secondary factor or factors over-and- above the one of interest. This issue, known as test bias, has been the subject of a great deal of recent research, and a technique called Differential Item Functioning (DIF) analysis has become the new standard in psychometric bias analysis. The purpose of this handbook is to provide a perspective and foundation for DIF, a review and recommendation of a family of statistical techniques (i.e., logistic regression) to conduct DIF analyses, and a series of examples to motivate its use in practice. DIF statistical techniques are based on the principle that if different groups of test-takers (e.g., males and females) have roughly the same level of something (e.g., knowledge), then they should perform similarly on individual test items regardless of group membership. In their essence, all DIF techniques match test takers from different groups according to their total test scores and then investigate how the different groups performed on individual test items to determine whether the test items are creating problems for a particular group.

Interoceptive sensitivity, particularly regarding heartbeat, has been suggested to play a pivotal role in the pathogenesis of anxiety and anxiety disorders. This review provides an overview of methods which are frequently used to assess heartbeat perception in clinical studies and summarizes presently available results referring to interoceptive sensitivity with respect to heartbeat in anxiety-related traits (anxiety sensitivity, state/trait anxiety), panic disorder and other anxiety disorders. In addition, recent neurobiological studies of neuronal activation correlates of heartbeat perception using positron emission tomography (PET), functional magnetic resonance imaging (fMRI) or electroencephalographic (EEG) techniques are presented. Finally, possible clinical and therapeutic implications (e.g., beta-blockers, biofeedback therapy, cognitive interventions and interoceptive exposure) of the effects of heartbeat perception on anxiety and the anxiety disorders and the potential use of interoceptive sensitivity as an intermediate phenotype of anxiety disorders in future neurobiological and genetic studies are discussed.

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individual genes. Here we apply bootstrapping to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about the reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments.

The following paper presents current thinking and research on fit indices for structural equation modelling. The paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers. As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting strategies of these indices and concludes with a discussion on the future of fit indices.

This paper examines the use of vignettes in cognitive particular living situations, and to provide us with interviews as a means of examining this evidence of the way respondents use residence terms. implicit social knowledge. Our intent was to investigate The first aim was accomplished by choosing situations in a broad range of naturally occurring terms and concepts, which we believed that respondents might have difficulty and not just those which appear in the roster questions in defining...

Recent research has developed various promising methods to control for population structure in genomewide association mapping of complex traits, but systematic examination of how well these methods perform under different genetic scenarios is still lacking. Appropriate methods for controlling genetic relationships among individuals need to balance the concern of false positives and statistical power, which can vary for different association sample types. We used a series of simulated samples and empirical data sets from cross- and self-pollinated species to demonstrate the performance of several contemporary methods in correcting for different types of genetic relationships encountered in association analysis. We proposed a two-stage dimension determination approach for both principal component analysis and nonmetric multidimensional scaling (nMDS) to capture the major structure pattern in association mapping samples. Our results showed that by exploiting both genotypic and phenotypic information, this two-stage dimension determination approach balances the trade-off between data fit and model complexity, resulting in an effective reduction in false positive rate with minimum loss in statistical power. Further, the nMDS technique of correcting for genetic relationship proved to be a powerful complement to other existing methods. Our findings highlight the significance of appropriate application of different statistical methods for dealing with complex genetic relationships in various genomewide association studies.

Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

Gender differences in social support tend to suggest that women have larger social networks and both give and receive more support than men. Nevertheless, although social support has been identified as protective of mental health, women have higher rates of psychological distress than men. We examine the prospective association between social support and psychological distress by gender in a cohort study of middle aged British Civil Servants, the Whitehall II study. In this sample we found that women have a larger number of close persons than men although men have larger social networks. We also found that the effects of marital status, social support within and outside the workplace and social networks on subsequent occurrence of psychological distress were similar for men and women independently of baseline mental health status.

This paper presents a historical view of the well-known k-means al- gorithm that aims at minimizing (approximately) the classical SSQ or variance criterion in cluster analysis . We show to which authors the different (discrete and continuous) versions of this algorithm can be traced back, and which were the underlying applications. Moreover, the paper describes a series of extensions and generalizations of this algorithm (for fuzzy clustering, maximum likelihood cluster- ing, convexity-based criteria,...) that shows the importance and usefulness of the k-means approach and related alternating minimization techniques in data analysis.

Historical episodes of natural selection can skew the frequencies of genetic variants, leaving a signature that can persist for many tens or even hundreds of thousands of years. However, formal tests for selection based on allele frequency skew require strong assumptions about demographic history and mutation, which are rarely well understood. Here, we develop an empirical approach to test for signals of selection that compares patterns of genetic variation at a candidate locus with matched random regions of the genome collected in the same way. We apply this approach to four genes that have been implicated in syndromes of impaired neurological development, comparing the pattern of variation in our re-sequencing data with a large-scale, genomic data set that provides an empirical null distribution. We confirm a previously reported signal at FOXP2, and find a novel signal of selection centered at AHI1, a gene that is involved in motor and behavior abnormalities. The locus is marked by many high frequency derived alleles in non-Africans that are of low frequency in Africans, suggesting that selection at this or a closely neighboring gene occurred in the ancestral population of non-Africans. Our study also provides a prototype for how empirical scans for ancient selection can be carried out once many genomes are sequenced.

BACKGROUND: Several studies showed a different symptom structure underlying the spectrum of autistic-like disorders from the behaviour triad as mentioned in the DSM-IV. In the present study, a hypothesised symptom model for autism was constructed, based on earlier explorative findings, and was put to a confirmatory test. METHOD: Items from the Autism Diagnostic Interview-Revised (ADI-R) were used to examine the goodness of fit of the DSM-IV model, the hypothesised symptom model, and two additional models for autism. All models were tested in a group of 255 verbal and nonverbal individuals with minor to severe autistic symptomatology. RESULTS: The DSM-IV model encountered estimation problems. Conversely, the hypothesised symptom model had no such problems and proved to have a better fit to the sample data than the two additional models for autism. However, some of the observed variables were weak indicators of the three latent factors in the model. CONCLUSIONS: The hypothesised symptom model appeared to be a plausible model in a group of individuals with a broad range of autistic behaviours and levels of functioning. Nevertheless, the stability of the model needs further examination in a larger group of individuals with disorders in the autism spectrum, and with varying degrees of intellectual functioning.

Objective: Results of reliability and agreement studies are intended to provide information about the amount of error inherent in any diagnosis, score, or measurement. The level of reliability and agreement among users of scales, instruments, or classifications is widely unknown. Therefore, there is a need for rigorously conducted interrater and intrarater reliability and agreement studies. Information about sample selection, study design, and statistical analysis is often incomplete. Because of inadequate reporting, interpretation and synthesis of study results are often difficult. Widely accepted criteria, standards, or guidelines for reporting reliability and agreement in the health care and medical field are lacking. The objective was to develop guidelines for reporting reliability and agreement studies. Study Design and Setting: Eight experts in reliability and agreement investigation developed guidelines for reporting. Results: Fifteen issues that should be addressed when reliability and agreement are reported are proposed. The issues correspond to the headings usually used in publications. Conclusion: The proposed guidelines intend to improve the quality of reporting.

BACKGROUND: Indices of physical function may have a hierarchy of items. In cases where this can be demonstrated it may be possible to reduce patient burden by asking them to complete only those items which relate directly to their own level of ability. OBJECTIVES: To determine whether statistical procedures, operationalising what is known as item response theory (IRT), can be used to assess the unidimensionality of the 10 item physical functioning domain of the SF-36 in patients with Parkinson's disease and motor neuron disease, and, secondly, to determine whether it would be possible to administer subsets of items to certain patients, on the basis of their replies to other items in the scale, thereby reducing patient burden. METHODS: Rasch analysis, a form of IRT methodology, of the 10 item physical functioning domain (PF-10) in two neurological patient samples was undertaken and the results compared with results of a Rasch analysis of data gained from a population survey (the third Oxford healthy lifestyles survey). RESULTS: Evidence from the analyses suggests that the PF-10 does not form a perfect hierarchy on a unidimensional scale. However, certain items seem to form a hierarchy, and responses to some of them are contingent on responses to the other items. CONCLUSIONS: Rasch analysis of the PF-10 in neurological patients has indicated that certain items of the scale are hierarchically ordered, and consequently not all respondents would need to complete them all: indeed those most severely ill would be required to complete less items than those with only limited disabilities. The implications of this are discussed.

This study aimed at developing a shortened version of the EORTC QLQ-C30, one of the most widely used health-related quality of life questionnaires in oncology, for palliative care research. The study included interviews with 41 patients and 66 health care professionals in palliative care to determine the appropriateness, relevance and importance of the various domains of the QLQ-C30. Item response theory methods were used to shorten scales. Patients and health care professionals rated pain, physical function, emotional function, fatigue, global health status/quality of life, nausea/vomiting, appetite, dyspnoea, constipation, and sleep as most important. Therefore, these scales/items were retained in the questionnaire. Four scales were shortened without reducing measurement precision. Important dimensions not covered by the questionnaire were identified. The resulting 15-item EORTC QLQ-C15-PAL is a 'core questionnaire' for palliative care. Depending on the research questions, it may be supplemented by additional items, modules or questionnaires.

BACKGROUND: The goal of much care in chronic childhood illness is to improve quality of life (QOL). However, surveys suggest QOL measures are not routinely included. In addition, there is little consensus about the quality of many QOL measures. OBJECTIVES: To determine the extent to which quality of life (QOL) measures are used in paediatric clinical trials and evaluate the quality of measures used. DESIGN: Systematic literature review. REVIEW METHODS: Included paediatric trials published in English between 1994 and 2003 involving children and adolescents up to the age of 20 years, and use of a standardised QOL measure. Data Sources included MEDLINE, CINAHL, EMB Reviews, AMED, BNI, PSYCHINFO, the Cochrane library, Internet, and reference lists from review articles. RESULTS: We identified 18 trials including assessment of QOL (4 Asthma, 4 Rhinitis, 2 Dermatitis, and single studies of Eczema, Cystic fibrosis, Otis media, Amblyopia, Diabetes, Obesity associated with a brain tumour, Idiopathic short stature, and Congenital agranulocytosis). In three trials, parents rated their own QOL but not their child's. Fourteen different QOL measures were used but only two fulfilled our minimal defined criteria for quality. CONCLUSIONS: This review confirms previous reports of limited use of QOL measures in paediatric clinical trials. Our review provides information about availability and quality of measures which will be of especial value to trial developers.

BACKGROUND: Despite consensus about the importance of measuring quality of diabetes care and the widespread use of self-reports and medical records to assess quality, little is known about the degree of agreement between these data sources. OBJECTIVES: To evaluate agreement between self-reported and medical record data on annual eye examinations and to identify factors associated with agreement. RESEARCH DESIGN AND SUBJECTS: Data from interviews and medical records were available for 8409 adults with diabetes who participated in the baseline round of the Translating Research Into Action for Diabetes (TRIAD) Study. MEASURES: Agreement between self-reports and medical records was evaluated as concordance and Cohen's kappa coefficient. RESULTS: Self-reports indicated a higher performance of annual dilated eye examinations than did medical records (75.9% vs. 38.8%). Concordance between the data sources was 57.9%. Agreement was only fair (kappa coefficient = 0.25; 95% confidence interval, 0.23-0.26). Nearly two-thirds (64.6%) of discordance was due to lack of evidence in the medical record to support self-reported performance of the procedure. After adjustment, agreement was most strongly related to health plan (chi = 977.9, df = 9; P < 0.0001), and remained significantly better for 3 of the 10 health plans (P < 0.00001) and for persons younger than 45 years of age (P = 0.00002). CONCLUSIONS: The low level of agreement between self-report and medical records suggests that many providers of diabetes care do not have easily available accurate information on the eye examination status of their patients.

BACKGROUND: This paper examines the distribution of the temperamental characteristics and gender effects of a new autoquestionnaire developed by Akiskal et al. (TEMPS-A) in its German briefTEMPS-M version. METHODS: As described in a companion article [J. Affect. Disord. 85 (2005), 53, this issue], based on a study population of 1056 students of the Westfälische-Wilhelms-Universität in Münster, Germany, we constructed the briefTEMPS-M. In the present paper we report on the basic descriptive statistics of the five subscales of the briefTEMPS (depressive, cyclothymic, hyperthymic, irritable, and anxious), as well as gender differences. RESULTS: Except for the hyperthymic, these subscales are capable of representing the full range of temperament in a sample of German students. Characteristics of the distribution (skewness, kurtosis) of the subscales are well in acceptable limits. We found higher depressive, cyclothymic, and anxious, as well as lower hyperthymic, temperament values in women as compared to men. Cut-off scores to determine extreme groups are provided. To render our results comparable to a similar study using the interview version of the TEMPS-I in an Italian student population [J. Affect. Disord. 47 (1998) 1; J. Affect. Disord. 51 (1998) 7], we computed the rates for dominant temperaments based on the z scores +2 S.D., and obtained the following: depressive, 4.7%; cyclothymic, 4.7%; hyperthymic, 2.1%; irritable, 4.0%; and anxious, 4.2%. CONCLUSIONS: The briefTEMPS-M is a potentially valuable scale to quickly assess temperament in research, clinical and normal samples.

Results from analyses of twin data that use models assuming a bivariate distribution of twin values will change when twins within pairs are reordered. We examined the effect of twin pair ordering on additive genetic variance estimates and hypothesis tests, from a bivariate normal model, both via simulation and through examination of real twin data. The simulations generated twin data for varying sample sizes and amounts of additive genetic and common environmental variance. The real data sets had sample sizes of 60 or less per zygosity. The results indicate that for moderate or large size studies, the effects of twin pair ordering are unlikely to greatly change the results of the data analysis; but for small studies the results can be sensitive to twin pair ordering. We therefore suggest that methods, not sensitive to within twin-pair differences be compared to the results obtained from twin-pair ordering. Methods not influenced by twin-pair ordering include least squares methods or covariance matrices approaches as described by Carey (2005, Behav Genet 35:667-670) or Guo and Wang (2002, Behav Genet 32:37-49).

Including all relevant material - good, bad, and indifferent - in meta -analysis admits the subjective judgments that meta-analysis was designed to avoid. Several problems arise in meta-analysis: regressions are often non -linear; effects are often multivariate rather than univariate; coverage can be restricted; bad studies may be included; the data summarised may not be homogeneous; grouping different causal factors may lead to meaningless estimates of effects; and the theory-directed approach may obscure discrepancies. Meta-analysis may not be the one best method for studying the diversity of fields for which it has been used. Why do we undertake systematic reviews of a given field? The most important reason is perhaps that we are concerned about a particular theory and wish to know how the evidence for and against stacks up. There are also practical reasons; single studies often use small numbers of subjects, and basing our estimates of effect sizes on large numbers of studies drastically lowers the fiducial limits around our estimates. Systematic reviews can be of several different kinds: traditional reviews, often not very systematic, and frequently biased; meta-analyses, including (we hope) all relevant material, good, bad, and different, and leading to an estimate of effect size*RF 1-3*; best-evidence synthesis4; and the hypothetico-deductive approach,5 in which the effort is directed at evaluating the evidence for and against a given theory, in an attempt to solve the problem of why contradictory results appear, rather than simply averaging often incompatible data.

A method is described to estimate variance and covariance components in a multiple trait situation with one continuous and one binary trait. An underlying bivariate normal distribution is assumed with one variable dichotomized on the observable scale through a fixed threshold. A mixed linear model is applied to the underlying scale, and Bayesian arguments are employed to derive estimation procedures for both location and dispersion parameters. This leads to a nonlinear system of equations similar to the mixed model equations for observations that have been transformed by a Cholesky decomposition of the residual variance-covariance matrix so that the residual covariance between the two transformed traits is zero, thereby simplifying construction of the multiple trait mixed model equations. The procedures for estimating genetic variances and covariances and the residual variance for the continuous trait are equivalent to restricted maximum likelihood in the multivariate normal case. The residual correlation is estimated using a maximum likelihood approach. Suitable computing strategies are indicated and a simulation study is giventoillustratetheuseofthemethod.Theimpactofsmallsubclasssizeontheestimates is seen to be a serious drawback to the proposed method. Possible generalizations of the method and potential problems in its practical application are discussed.

ABSTRACT: BACKGROUND: : Fast-acting medications for the management of anxiety are important to patients and society. Measuring early onset, however, requires a sensitive and clinically responsive tool. This study evaluates the psychometric properties of a patient-reported Global Anxiety - Visual Analog Scale (GA-VAS). METHOD: : Data from a double-blind, randomized, placebo-controlled study of lorazepam and paroxetine in patients with Generalized Anxiety Disorder were analyzed to assess the reliability, validity, responsiveness, and utility of the GA-VAS. The GA-VAS was completed at clinic visits and at home during the first week of treatment. Targeted psychometric analyses - test-retest reliabilities, validity correlations, responsiveness statistics, and minimum important differences - were conducted. RESULTS: : The GA-VAS correlates well with other anxiety measures, at Week 4, r=0.60 (p<0.0001) with the Hamilton Rating Scale for Anxiety and r=0.74 (p<0.0001) with the Hospital Anxiety and Depression Scale - Anxiety subscale. In terms of convergent and divergent validity, the GA-VAS correlated -0.54 (p<0.0001), -0.48 (p<0.0001), and -0.68 (p<0.0001) with the SF-36 Emotional Role, Social Function, and Mental Health subscales, respectively, but correlated much lower with the SF-36 physical functioning subscales. Preliminary minimum important difference estimates cluster between 10 and 15 mm. CONCLUSIONS: : The GA-VAS is capable of validly and effectively capturing a reduction in anxiety as quickly as 24 hours post-dose.

The ability to grasp emotional messages in everyday gestures and respond to them is at the core of successful social communication. The hypothesis that abnormalities in socio-emotional behavior in people with autism are linked to a failure to grasp emotional significance conveyed by gestures was explored. We measured brain activity using fMRI during perception of fearful or neutral actions and showed that whereas similar activation of brain regions known to play a role in action perception was revealed in both autistics and controls, autistics failed to activate amygdala, inferior frontal gyrus and premotor cortex when viewing gestures expressing fear. Our results support the notion that dysfunctions in this network may contribute significantly to the characteristic communicative impairments documented in autism.

ABSTRACT: Although the literature concerning statistical testing for genotype-phenotype association in family-based and population-based studies is very extensive, until recently the sex chromosomes have received little attention. Here it is shown that the X chromosome in particular presents special problems with respect to efficient analysis of mixed-sex population studies, and as a result of X inactivation. This paper reviews recent developments in approaching these problems.

BACKGROUND: Patient-reported outcomes (PROs) are the consequences of disease and/or its treatment as reported by the patient. The importance of PRO measures in clinical trials for new drugs, biological agents, and devices was underscored by the release of the US Food and Drug Administration's draft guidance for industry titled "Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims." The intent of the guidance was to describe how the FDA will evaluate the appropriateness and adequacy of PRO measures used as effectiveness end points in clinical trials. In response to the expressed need of ISPOR members for further clarification of several aspects of the draft guidance, ISPOR's Health Science Policy Council created three task forces, one of which was charged with addressing the implications of the draft guidance for the collection of PRO data using electronic data capture modes of administration (ePRO). The objective of this report is to present recommendations from ISPOR's ePRO Good Research Practices Task Force regarding the evidence necessary to support the comparability, or measurement equivalence, of ePROs to the paper-based PRO measures from which they were adapted. METHODS: The task force was composed of the leadership team of ISPOR's ePRO Working Group and members of another group (i.e., ePRO Consensus Development Working Group) that had already begun to develop recommendations regarding ePRO good research practices. The resulting task force membership reflected a broad array of backgrounds, perspectives, and expertise that enriched the development of this report. The prior work became the starting point for the Task Force report. A subset of the task force members became the writing team that prepared subsequent iterations of the report that were distributed to the full task force for review and feedback. In addition, review beyond the task force was sought and obtained. Along with a presentation and discussion period at an ISPOR meeting, a draft version of the full report was distributed to roughly 220 members of a reviewer group. The reviewer group comprised individuals who had responded to an emailed invitation to the full membership of ISPOR. This Task Force report reflects the extensive internal and external input received during the 16-month good research practices development process. RESULTS/RECOMMENDATIONS: An ePRO questionnaire that has been adapted from a paper-based questionnaire ought to produce data that are equivalent or superior (e.g., higher reliability) to the data produced from the original paper version. Measurement equivalence is a function of the comparability of the psychometric properties of the data obtained via the original and adapted administration mode. This comparability is driven by the amount of modification to the content and format of the original paper PRO questionnaire required during the migration process. The magnitude of a particular modification is defined with reference to its potential effect on the content, meaning, or interpretation of the measure's items and/or scales. Based on the magnitude of the modification, evidence for measurement equivalence can be generated through combinations of the following: cognitive debriefing/testing, usability testing, equivalence testing, or, if substantial modifications have been made, full psychometric testing. As long as only minor modifications were made to the measure during the migration process, a substantial body of existing evidence suggests that the psychometric properties of the original measure will still hold for the ePRO version. Hence, an evaluation limited to cognitive debriefing and usability testing only may be sufficient. However, where more substantive changes in the migration process has occurred, confirming that the adaptation to the ePRO format did not introduce significant response bias and that the two modes of administration produce essentially equivalent results is necessary. Recommendations regarding the study designs and statistical approaches for assessing measurement equivalence are provided. CONCLUSIONS: The electronic administration of PRO measures offers many advantages over paper administration. We provide a general framework for decisions regarding the level of evidence needed to support modifications that are made to PRO measures when they are migrated from paper to ePRO devices. The key issues include: 1) the determination of the extent of modification required to administer the PRO on the ePRO device and 2) the selection and implementation of an effective strategy for testing the measurement equivalence of the two modes of administration. We hope that these good research practice recommendations provide a path forward for researchers interested in migrating PRO measures to electronic data collection platforms.

ABSTRACT: BACKGROUND: The phosphodiesterase 4D (PDE4D) gene was reported as a susceptibility gene to stroke. The genetic effect might be attributed to its role in modulating the atherogenic process in the carotid arteries. Using carotid intima-media thickness (IMT) and plaque index as phenotypes, the present study sought to determine the influence of this gene on subclinical atherosclerosis. METHODS: Carotid ultrasonography was performed on 1013 stroke-free subjects who participated in the health screening programs at the Kaohsiung Medical University Hospital (age 52.6 +- 12.2; 47.6% men). Genotype distribution was compared among high- (plaque index>= 4), low-risk (index=1-3), and reference (index=0) groups. We analyzed continuous IMT data and further dichotomized IMT data using mean plus one standard deviation as the cutoff level. Because the plaque prevalence and IMT values displayed a notable difference between men and women, we carried out sex-specific analyses in addition to analyzing the overall data. Rs702553 at the PDE4D gene was selected because it confers a risk for young stroke in our previous report. Previous young stroke data (190 cases and 211 controls) of additional 532 control subjects without ultrasonic data were shown as a cross-validation for the genetic effect. RESULTS: In the overall analyses, the rare homozygote of rs702553 led to OR of 3.1 (p = 0.034) for a plaque index >= 4. When subjects were stratified by sex, the genetic effect was only evident in men but not in women. Comparing male subjects with plaque index >= 4 and plaque index = 0, the TT genotype was over-represented (27.6% vs. 13.4%, p = 0.008). For dichotomized IMT data in men, the TT genotype had an OR of 2.1 (p = 0.032) for a thicker IMT at the common carotid artery compared with the (AA+AT) genotypes. In women, neither IMT nor plaque index was associated with rs702553. Similarly, SNP rs702553 was only significant in young stroke men (OR=1.8, p=0.025) but not in women (p= 0.27). CONCLUSIONS: The present study demonstrates a sex-differential effect of PDE4D on IMT, plaque index and stroke, which highlights its influence on various aspects of atherogenesis.

Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous outcome, and 10 covariates were simulated under seven scenarios differing by degree of non-linear and non-additive associations between covariates and the exposure. Propensity score weights were estimated using logistic regression (all main effects), CART, pruned CART, and the ensemble methods of bagged CART, random forests, and boosted CART. Performance metrics included covariate balance, standard error, per cent absolute bias, and 95 per cent confidence interval (CI) coverage. All methods displayed generally acceptable performance under conditions of either non-linearity or non-additivity alone. However, under conditions of both moderate non-additivity and moderate non-linearity, logistic regression had subpar performance, whereas ensemble methods provided substantially better bias reduction and more consistent 95 per cent CI coverage. The results suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting. Copyright (c) 2009 John Wiley & Sons, Ltd.

This study aimed to test the assumption drawn from weak central coherence theory that a central cognitive mechanism is responsible for integrating information at both conceptual and perceptual levels. A visual semantic memory task and a face recognition task measuring use of holistic information were administered to 15 children with autism and 16 typically developing children. If there is a central integration mechanism, performance on the two tasks should be positively associated. No relationship was found, however, between the two abilities in the comparison group and, unexpectedly, a strong significant inverse correlation was found in the autism group. Classification data further confirmed this finding and indicated the possibility of the presence of subgroups in autism. The results add to emerging evidence suggesting that central coherence is not a unitary construct.

Latent class and the Grade of Membership (GoM) models are two examples of latent structure models. Latent class models are discrete mixture models. The GoM model has been originally developed as an extension of latent class models to a continuous mixture. This note describes a constrained latent class model which is equivalent to the GoM model, and provides a detailed proof of this equivalence. Implications for model fitting and interpretation are discussed.

Most organisms display remarkable differences in morphological, anatomical, and developmental features between the two sexes. It has been recognized that these sex-dependent differences are controlled by an array of specific genetic factors, mediated through various environmental stimuli. In this paper, we present a unifying statistical model for mapping quantitative trait loci (QTL) that are responsible for sexual differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, incorporated by sex-stimulated differentiation in growth form that is described by mathematical functions. A typical structural model is implemented to approximate time-dependent covariance matrices for longitudinal traits. This model allows for a number of biologically meaningful hypothesis tests regarding the effects of QTL on overall growth trajectories or particular stages of development. It is particularly powerful to test whether and how the genetic effects of QTL are expressed differently in different sexual backgrounds. Our model has been employed to map QTL affecting body mass growth trajectories in both male and female mice of an F2 population derived from the large (LG/J) and small (SM/J) mouse strains. We detected four growth QTL on chromosomes 6, 7, 11, and 15, two of which trigger different effects on growth curves between the two sexes. All the four QTL display significant genotype-sex interaction effects on the timing of maximal growth rate in the ontogenetic growth of mice. The implications of our model for studying the genetic architecture of growth trajectories and its extensions to some more general situations are discussed.

Genome-wide association study for complex diseases will generate massive amount of single nucleotide polymorphisms (SNPs) data. Univariate statistical test (i.e. Fisher exact test) was used to single out non-associated SNPs. However, the disease-susceptible SNPs may have little marginal effects in population and are unlikely to retain after the univariate tests. Also, model-based methods are impractical for large-scale dataset. Moreover, genetic heterogeneity makes the traditional methods harder to identify the genetic causes of diseases. A more recent random forest method provides a more robust method for screening the SNPs in thousands scale. However, for more large-scale data, i.e., Affymetrix Human Mapping 100K GeneChip data, a faster screening method is required to screening SNPs in whole-genome large scale association analysis with genetic heterogeneity. We propose a boosting-based method for rapid screening in large-scale analysis of complex traits in the presence of genetic heterogeneity. It provides a relatively fast and fairly good tool for screening and limiting the candidate SNPs for further more complex computational modeling task.

Empowered by technology and sampling efforts designed to facilitate genome-wide association mapping, human geneticists are now studying the geography of genetic variation in unprecedented detail. With high genomic coverage and geographic resolution, these studies are identifying loci with spatial signatures of selection, such as extreme levels of differentiation and correlations with environmental variables. Collectively, patterns at these loci are beginning to provide new insights into the process of human adaptation. Here, we review the challenges of these studies and emerging results, including how human population structure has influenced the response to novel selective pressures.

Personality traits are summarized by five broad dimensions with pervasive influences on major life outcomes, strong links to psychiatric disorders and clear heritable components. To identify genetic variants associated with each of the five dimensions of personality we performed a genome-wide association (GWA) scan of 3972 individuals from a genetically isolated population within Sardinia, Italy. On the basis of the analyses of 362 129 single-nucleotide polymorphisms we found several strong signals within or near genes previously implicated in psychiatric disorders. They include the association of neuroticism with SNAP25 (rs362584, P=5 x 10(-5)), extraversion with BDNF and two cadherin genes (CDH13 and CDH23; Ps<5 x 10(-5)), openness with CNTNAP2 (rs10251794, P=3 x 10(-5)), agreeableness with CLOCK (rs6832769, P=9 x 10(-6)) and conscientiousness with DYRK1A (rs2835731, P=3 x 10(-5)). Effect sizes were small (less than 1% of variance), and most failed to replicate in the follow-up independent samples (N up to 3903), though the association between agreeableness and CLOCK was supported in two of three replication samples (overall P=2 x 10(-5)). We infer that a large number of loci may influence personality traits and disorders, requiring larger sample sizes for the GWA approach to confidently identify associated genetic variants.Molecular Psychiatry advance online publication, 28 October 2008; doi:10.1038/mp.2008.113.

We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.

How does one determine if a measure of health-related quality of life (HRQL) is adequate for clinical trials? Psychometric methods are frequently used to answer this question. What is psychometrics all about? In this paper we address these questions, discussing common psychometric evaluation procedures applied to HRQL measures. Specifically, we discuss issues regarding the evaluation of reliability and validity (including responsiveness).

Principal-component analyses of 4 face-recognition studies uncovered 2 independent components. The first component was strongly related to false-alarm errors with new faces as well as to facial "conjunctions" that recombine features of previously studied faces. The second component was strongly related to hits as well as to the conjunction/new difference in false-alarm errors. The pattern of loadings on both components was impressively invariant across the experiments, which differed in age range of participants, stimulus set, list length, facial orientation, and the presence versus absence of familiarized lures along with conjunction and entirely new lures in the recognition test. Taken together, the findings show that neither component was exclusively related to discrimination, criterion, configural processing, featural processing, context recollection, or familiarity. Rather, the data are consistent with a neuropsychological model that distinguishes frontal and occipitotemporal contributions to face recognition memory. Within the framework of the model, findings showed that frontal and occipitotemporal contributions are discernible from the pattern of individual differences in behavioral performance among healthy young adults.

Discretization acts as a variable selection method in addition to transforming the continuous values of the variable to discrete ones. Machine learning algorithms such as Support Vector Machines and Random Forests have been used for classification in high-dimensional genomic and proteomic data due to their robustness to the dimensionality of the data. We show that discretization can help improve significantly the classification performance of these algorithms as well as algorithms like Naïve Bayes that are sensitive to the dimensionality of the data.

This study investigated the performance of Poly-DIMTEST (PD) to assess unidimensionality of test data produced by polytomous items. Two types of polytomous data were considered: (1) tests in which all items had the same number of response categories, and (2) tests in which items had a mixed number of response categories. Test length, sample size, and the type of correlation matrix (used in factor analysis for selecting ATI subset items) were varied in Type I error analyses. For the power study, the correlation between Os and the item-0 loadings were also varied. The results showed that PD was able to confirm unidimensionality for unidimensional simulated test data, with the average observed level of significance slightly below the nominal level. PD was also highly effective in detecting lack of unidimensionality in various two-dimensional tests. As expected, power increased as the sample size and test length increased, and the correlation between the Os decreased. The results also demonstrated that use of Pearson correlations to select ATI items led to equally good or better performance than using polychoric correlations; therefore Pearson correlations are recommended for future use.

ABSTRACT: BACKGROUND: The Multidimensional Fatigue Inventory (MFI-20) was developed in 1995. Since then, it has been widely used in cancer research and cancer-related illnesses but has never been validated in fatiguing illnesses or in a large US population-selected sample. In this study, we sought to examine the reliability and validity of the MFI-20 in the population of the state of Georgia, USA. Further, we assessed whether the MFI-20 could serve as a complementary diagnostic tool in chronically fatigued and unwell populations. METHODS: The data derive from a cross-sectional population-based study investigating the prevalence of chronic fatigue syndrome (CFS) in Georgia. The study sample was comprised of three diagnostic groups: CFS-like (292), chronically unwell (269), and well (222). Participants completed the MFI-20 along with several other measures of psychosocial functioning, including the Medical Outcomes Survey Short Form-36 (SF-36), the Zung Self-Rating Depression Scale (SDS), and the Spielberger State-Trait Anxiety Inventory (STAI). We assessed the five MFI-20 subscales using several criteria: inter-item correlations, corrected item-total correlations, internal consistency reliability (Cronbach's alpha coefficients), construct validity, discriminant (known-group) validity, floor/ceiling effects, and convergent validity through correlations with the SF-36, SDS, and STAI instruments. RESULTS: Averaged inter-item correlations ranged from 0.38 to 0.61, indicating no item redundancy. Corrected item-total correlations for all MFI-20 subscales were greater than 0.30, and Cronbach's alpha coefficients achieved an acceptable level of 0.70. No significant floor/ceiling effect was observed. Factor analysis demonstrated factorial complexity. The MFI-20 also distinguished clearly between three diagnostic groups on all subscales. Furthermore, correlations with depression (SDS), anxiety (STAI), and functional impairment (SF-36) demonstrated strong convergent validity. CONCLUSIONS: This study provides support for the MFI-20 as a valuable tool when used in chronically unwell and well populations. It also suggests that the MFI-20 could serve as a complementary diagnostic tool in fatiguing illnesses, such as CFS.

CONTEXT: The term "patient-reported outcomes" (PROs) has evolved to include any endpoint derived from patient reports, whether collected in the clinic, in a diary, or by other means, including single-item outcome measures, event logs, symptom reports, formal instruments to measure health-related quality of life (HRQL), health status, adherence, and satisfaction with treatment. This term coincides with the explicit interest from drug development researchers and regulatory authorities in the appropriate utilization and reporting of treatment impact measures. OBJECTIVE: To determine the level and nature of use of PROs compared to other types of effectiveness endpoints in approved product labeling for new drugs recently approved in the United States. DESIGN AND SOURCES: Review and analysis of effectiveness endpoints as reported in clinical study descriptions in approved product labeling of new molecular entities (NMEs) approved in the United States from 1997 through 2002. MAIN OUTCOME MEASURES: Effectiveness study endpoints reported in approved product labeling that fall into the following categories of measurement: PROs, clinician-reported outcomes (CROs), and laboratory test/device measurement endpoints. RESULTS: PROs were reported in 64 (30%) of the 215 product labels reviewed. Clinician-reported outcomes were reported most frequently (62%) followed by laboratory/device endpoints (50%). PROs were the only type of endpoint used in the FDA-approved label for 23 products. Formal multiitem PRO scales were cited 22 times. Use of PROs is most common in antiinflammatory, CNS, gastrointestinal, respiratory, allergic conjunctivitis, and urologic therapy areas. The frequency of reported PRO use over this period did not change. CONCLUSION: PROs, although quite variable as a class of study endpoints, were found to have a significant role in the development and evaluation of new medicines. More formal guidance from the FDA about use of such measures along with continued collaboration by PRO researchers to develop and disseminate standards will enhance the appropriate use of PROs in future drug development and labeling.

CONTEXT: We have reported elsewhere on the development of an 8-item Obsessive-Compulsive Scale (OCS) contained in the Child Behavior Checklist (CBCL) to identify children who meet criteria for DSM-IV obsessive-compulsive disorder. Twin studies of obsessive-compulsive disorder have indicated a significant genetic component to its expression. OBJECTIVE: To determine the relative contributions of genetic and environmental influences on childhood obsessive-compulsive behavior using the CBCL OCS in twin samples. DESIGN: The CBCL data were received by survey of twins in the Netherlands Twin Registry (NTR) and the Missouri Twin Study (USA/MOTWIN). SETTING: General community twin samples. PARTICIPANTS: Participants were 4246 twin pairs aged 7 years, 2841 aged 10 years, and 1562 aged 12 years (who also participated in the study at 7 and 10 years of age) from the NTR and 1461 mixed-age twin pairs (average age, approximately 9 years) from the USA/MOTWIN. MAIN OUTCOME MEASURES: Model fitting to test for genetic and environmental influences, sex differences, and sibling interaction/rater contrast effects on the CBCL OCS. RESULTS: In each case, the best-fitting model was one that indicated significant additive genetic influences (range, 45%-58%; 95% confidence interval [CI], 45%-61%), and unique environmental influences (range, 42%-55%; 95% CI, 39%-55%), with shared environmental influences in the NTR sample aged 12 years (16%). Sex differences were seen in the mixed-age USA/MOTWIN model, but not in the NTR samples. No evidence of dominance, sibling interaction, or rater-contrast effects was seen. These data were relatively consistent across age and cultures. CONCLUSIONS: The CBCL OCS is influenced by genetic factors (approximately 55%) and unique environmental factors (approximately 45%) in the younger sample, with common environmental influences only at 12 years of age. These effects do not vary with differences in sex or sibling interaction/rater contrast effects. Our data reveal higher genetic influences for obsessive-compulsive behavior and do not demonstrate genetic differences across sex.

A variety of Haseman-Elston type regression procedures were used to perform a genome scan across five chromosomes, using replicates 1-5 of the Genetic Analysis Workshop 13 simulated data. The traits of interest were variables corresponding to 'baseline' and 'slope' effects derived from the fasting glucose phenotypes. Performance in terms of detecting the locations of known trait loci was poor for all methods, even when all five replicates were combined to produce a large data set (9230 sib pairs). All methods performed well, however, when applied to new simulated data in which the true genetic effects were allowed to explain a greater proportion of the overall variance.

In the last decade a growing amount of statistical research has been devoted to multiple testing, motivated by a variety of applications in medicine, bioinformatics, genomics, brain imaging, etc. Research in this area is focused on developing powerful procedures even when the number of tests is very large. This paper attempts to review research in modern multiple hypothesis testing with particular attention to the false discovery proportion, loosely defined as the number of false rejections divided by the number of rejections. We review the main ideas, stepwise and augmentation procedures; and resampling based testing. We also discuss the problem of dependence among the test statistics. Simulations make a comparison between the procedures and with Bayesian methods. We illustrate the procedures in applications in DNA microarray data analysis. Finally, few possibilities for further research are highlighted.

Recently, microarrays technologies have been extensively used to distinguish gene expression in acute lymphoblastic leukaemia (ALL) (e.g. Pui et al., 2004; Hoffmann et al., 2008). ALL is the most common type of leukaemia diagnosed in children, with an incidence rate of about 4 per 100,000 per year (Pizzo and Poplack, 2001; Milne et al., 2008). There are six main subtypes of leukaemia, one of which is T- cell acute lymphoblastic leukaemia (T-ALL) which generally has lower cure rates than other forms of ALL. Ribonucleic acid (RNA) samples from each patient can be put onto microarrays to provide gene expression levels for around 20 thousand genes (depending on which microarray chip is used). One of the challenges with microarray analysis in leukaemia research is identifying the smallest possible set of genes that predict relapse with the highest predictive performance.

Normative comparisons are a procedure for evaluating the clinical significance of therapeutic interventions. Although a step-by-step statistical methodology for conducting normative comparisons has been reported elsewhere (P. C. Kendall, A. Marrs-Garcia, S. R. Nath, & R. C. Sheldrick, 1999), questions regarding the collecting of normative data remain. For this study, all treatment outcome studies published in the Journal of Consulting and Clinical Psychology from 1988 to 1997 were examined and reviewed, and the 5 most commonly used outcome measures were identified. For these outcome measures, multiple sources of normative data were located. Although we identified a dearth of normative data on measures used for treatment outcome, results discussed here nevertheless provide information that may be of use to therapy outcome evaluators when conducting normative comparisons. In addition, equations to determine the minimum sample size needed in a normative sample for a given treatment outcome study are provided.

ABSTRACT: BACKGROUND: As computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only elucidate main effects of genetic variants on risk for disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. One such approach is the Random Forests (RF) algorithm. The use of RF for SNP discovery related to human disease has grown in recent years; however, most work has focused on small datasets or simulation studies which are limited. RESULTS: Using a multiple sclerosis (MS) case-control dataset comprised of  300K SNP genotypes across the genome, we outline an approach and some considerations for optimally tuning the RF algorithm based on the empirical dataset. Importantly, results show that typical default parameter values are not appropriate for large GWA datasets. Furthermore, gains can be made by sub-sampling the data, pruning based on linkage disequilibrium (LD), and removing strong effects from RF analyses. The new RF results are compared to findings from the original MS GWA study and demonstrate overlap. In addition, four new interesting candidate MS genes are identified, MPHOSPH9, CTNNA3, PHACTR2 and IL7, by RF analysis and warrant further follow-up in independent studies. CONCLUSIONS: This study presents one of the first illustrations of successfully analyzing GWA data with a machine learning algorithm. It is shown that RF is computationally feasible for GWA data and the results obtained make biologic sense based on previous studies. More importantly, new genes were identified as potentially being associated with MS, suggesting new avenues of investigation for this complex disease.

The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.

In biometrical genetic analyses of binary traits, the use of family data overcomes some limitations of twin studies, particularly in terms of sample size and types of genetic or environmental factors that can be estimated. However, because of computational problems, recent methods in the application of generalized linear mixed models for family data structure have limited the ability to handle large data sets with general covariates. In this paper, we investigate the use of the hierarchical likelihood approach to the analysis of binary traits from family data. In a simulation study, the method is shown to be highly accurate for the estimation of both the variance components and fixed regression parameters, even for small family sizes. For illustration, we analyze a real data set of familial aggregation of preeclampsia, a pregnancy-induced hypertension. When possible, the analysis is compared with the exact maximum likelihood approach.

OBJECTIVES: To describe psychological symptoms in 8-12-year-old children with cerebral palsy; to investigate predictors of these symptoms and their impact on the child and family. DESIGN: A cross-sectional multi-centre survey. PARTICIPANTS: Eight hundred and eighteen children with cerebral palsy, aged 8-12 years, identified from population-based registers of cerebral palsy in eight European regions and from multiple sources in one further region. MAIN OUTCOME MEASURES: The Strengths and Difficulties Questionnaire (SDQ)(P4-16) and the Total Difficulties Score (TDS) dichotomised into normal/borderline (TDS < or = 16) versus abnormal (TDS > 16). STATISTICAL ANALYSIS: Multilevel, multivariable logistic regression to relate the presence of psychological symptoms to child and family characteristics. RESULTS: About a quarter of the children had TDS > 16 indicating significant psychological symptoms, most commonly in the domain Peer Problems. Better gross motor function, poorer intellect, more pain, having a disabled or ill sibling and living in a town were independently associated with TDS > 16. The risk of TDS > 16 was odds ratio (OR) = .2 (95% CI: .1 to .3) comparing children with the most and least severe functional limitations; OR = 3.2 (95%CI: 2.1 to 4.8) comparing children with IQ < 70 and others; OR = 2.7 (95% CI: 1.5 to 4.6) comparing children in severe pain and others; OR = 2.7 (95% CI:1.6 to 4.6) comparing children with another disabled sibling or OR = 1.8 (95%CI: 1.2 to 2.8) no siblings and others; OR = 1.8 (95% CI: 1.1 to 2.8) comparing children resident in a town and others. Among parents who reported their child to have psychological problems, 95% said they had lasted over a year, 37% said they distressed their child and 42% said they burdened the family at least 'quite a lot'. CONCLUSIONS: A significant proportion of children with cerebral palsy have psychological symptoms or social impairment sufficiently severe to warrant referral to specialist services. Care must be taken in the assessment and management of children with cerebral palsy to ensure psychological problems are not overlooked and potentially preventable risk factors like pain are treated effectively. The validity of the SDQ for children with severe disability warrants further assessment.

Rating scales with detailed level descriptors are used in writing performance assessment to give raters an explicit basis on which to award scores. These scales, however, are generally constructed by a team of developers who rely largely on intuition of what they think writers produce rather than what they actually produce. The descriptors have furthermore been criticized for often being too vague to result in reliable ratings. In recent years there has been a call for more empirically based methods of scale development (Fulcher, 2003; Upshur & Turner, 1999), as this process should greatly improve the validity and the reliability of the ratings. The aim of this study is to investigate whether such an empirically developed rating scale for writing does in fact result in more reliable and valid ratings than more intuitively developed measures. The validation process involved 10 trained raters rating 100 scripts (produced as part of a large-scale diagnostic assessment administered to both native and nonnative speakers of English at a large university) using both sets of descriptors. The quantitative analysis was undertaken using FACETS. Questionnaires and interviews were also administered to elicit the raters' perceptions of the efficacy of the two types of scales. The results suggest that the raters used very different cognitive processes when employing the two sets of descriptors, resulting in two different score profiles. The findings are discussed in terms of their implications for rater training and rating scale development.

The expression level for 15,887 transcripts in lymphoblastoid cell lines from 19 monozygotic twin pairs (10 male, 9 female) were analysed for the effects of genotype and sex. On an average, the effect of twin pairs explained 31% of the variance in normalized gene expression levels, consistent with previous broad sense heritability estimates. The effect of sex on gene expression levels was most noticeable on the X chromosome, which contained 15 of the 20 significantly differentially expressed genes. A high concordance was observed between the sex difference test statistics and surveys of genes escaping X chromosome inactivation. Notably, several autosomal genes showed significant differences in gene expression between the sexes despite much of the cellular environment differences being effectively removed in the cell lines. A publicly available gene expression data set from the CEPH families was used to validate the results. The heritability of gene expression levels as estimated from the two data sets showed a highly significant positive correlation, particularly when both estimates were close to one and thus had the smallest standard error. There was a large concordance between the genes significantly differentially expressed between the sexes in the two data sets. Analysis of the variability of probe binding intensities within a probe set indicated that results are robust to the possible presence of polymorphisms in the target sequences.

MOTIVATION: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. RESULTS: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.

Acetylcholinesterase (AChE) plays a crucial physiological role in termination of impulse transmission at cholinergic synapses through rapid hydrolysis of acetylcholine. It is a highly conserved molecule, and only a few naturally occurring genetic polymorphisms have been reported in the human gene. The goal of the present study was to make a systematic effort to identify natural single nucleotide polymorphisms (SNPs) in the human ACHE gene. To this end, the genomic coding sequences for acetylcholinesterase of 96 unrelated control individuals from three distinct ethnic groups were analyzed. A total of 13 ACHE SNPs were identified, 10 of which are newly described, and five that should produce amino acid substitutions [c.101G>A (p.Arg34Gln), c.169G>A (p.Gly57Arg), c.1031A>G (p.Glu344Gly), c.1057C>A (p.His353Asn), and c.1775C>G (p.Pro592Arg)]. Population frequencies of 11 of the 13 SNPs were established in four different populations: African Americans, Ashkenazi Jews, Sephardic Jews, and Israeli Arabs; 15 haplotypes and five ethnospecific alleles were identified. The low number of SNPs identified until now in the ACHE gene is ascribed to technical hurdles arising from the high GC content and the presence of numerous repeat sequences, and does not reflect its intrinsic heterozygosity. Among the SNPs resulting in an amino acid substitution, three are within the mature protein, mapping on its external surface: they are thus unlikely to affect its catalytic properties, yet could have antigenic consequences or affect putative protein-protein interactions. Furthermore, the newly identified SNPs open the door to a study of the possible association of AChE with deleterious phenotypes-such as adverse drug responses to AChE inhibitors employed in treatment of Alzheimer patients and hypersensitivity to pesticides.

The recent introduction of the diagnostic category developmental coordination disorder (DCD) (American Psychiatric Association [APA], 1987, 1994), has generated confusion among researchers and clinicians in many fields, including occupational therapy. Although the diagnostic criteria appear to be similar to those used to define clumsy children, children with developmental dyspraxia, or children with sensory integrative dysfunction, we are left with the question: Are children who receive the diagnosis of DCD the same as those who receive the other diagnoses, a subgroup, or an entirely distinct group of children? This article will examine the theoretical and empirical literature and use the results to support the thesis that these terms are not interchangeable and yet are not being used in the literature in a way that clearly defines each subgroup of children. Clear definitions and characteristic features need to be identified and associated with each term to guide occupational therapy assessment and intervention and clinical research.

This report documents relationships between differential item functioning (DIF) identification and: (1) item-trait asso- ciation, and (2) scale multidimensionality in personality assessment. Applying [Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Ottawa, ON: Directorate of Human Resources Research and Evaluation, Depart- ment of National Defense.] logistic regression model, DIF effect size is found to become increasingly inflated as investi- gated item associations with trait scores decrease. Similar patterns were noted for the influence of scale multidimensionality on DIF identification. Individuals who investigate DIF in personality assessment applications are pro- vided with estimates regarding the impact of the magnitude of item and trait association and scale multidimensionality on DIF occurrence and effect size. The results emphasize the importance of excluding investigated items in focal trait identi- fication prior to conducting DIF analyses and reporting item and scale psychometric properties in DIF reports.

Ourconcernisselectingtheconcentrationmatrix'snonzeroco- efficients for a sparse Gaussian graphical model in a high-dimensional set- ting. This corresponds to estimating the graph of conditional dependencies between the variables. We describe a novel framework taking into account a latent structure on the concentration matrix. This latent structure is used to drive a penalty matrix and thus to recover a graphical model with a constrained topology. Our method uses an l1 penalized likelihood crite- rion. Inference of the graph of conditional dependencies between the vari- ates and of the hidden variables is performed simultaneously in an iterative em-like algorithm named SIMoNe (Statistical Inference for Modular Net- works). Performances are illustrated on synthetic as well as real data, the latter concerning breast cancer. For gene regulation networks, our method can provide a useful insight both on the mutual influence existing between genes, and on the modules existing in the network.

The predominant dimensional model of general personality structure within psychology is the five-factor model (FFM). Research indicates that the personality disorders of the American Psychiatric Association's diagnostic manual can be understood as maladaptive variants of the domains and facets of the FFM. The current review provides a proposal for the classification of personality disorder from the perspective of the FFM. Discussed as well are implications and issues associated with an FFM of personality disorder, including the integration of a psychiatric nomenclature with general personality structure, the inclusion of a domain of openness to experience, the identification of problems in living associated with maladaptive personality traits, the setting of a diagnostic threshold, prototypal matching, feasibility, and clinical utility.

Brain plasticity refers to changes in brain function and structure that arise in a number of contexts. One area in which brain plasticity is of considerable interest is recovery from stroke, both spontaneous and treatment-induced. A number of factors influence these poststroke brain events. The current review considers the impact of genetic factors. Polymorphisms in the human genes coding for brain-derived neurotrophic factor (BDNF) and apolipoprotein E (ApoE) have been studied in the context of plasticity and/or stroke recovery and are discussed here in detail. Several other genetic polymorphisms are indirectly involved in stroke recovery through their modulating influences on processes such as depression and pharmacotherapy effects. Finally, new genetic polymorphisms that have not been studied in the context of stroke are proposed as new directions for study. A better understanding of genetic influences on recovery and response to therapy might allow improved treatment after stroke.

Although writing clear questions is accepted as a general goal in surveys, procedures to ensure that each key term is consistently understood are not routine. Researchers who do not adequately test respondent understanding of questions must assume that ambiguity will not have a large or systematic effect on their results. Seven questions that were drawn from questions used in national health surveys were subjected to special pretest procedures and found to contain one or more poorly defined terms. When the questions were revised to clarify the definition of key terms, significantly different estimates resulted.The implication is that unclear terms are likely to produce biased estimates. The results indicate that evaluation of survey questions to identify key terms that are not consistently understood and defining unclear terms are ways to reduce systematic error in survey measurement.

Neuropsychiatric conditions such as autism and schizophrenia have long been attributed to genetic alterations, but identifying the genes responsible has proved challenging. Microarray experiments have now revealed abundant copy-number variation-a type of variation in which stretches of DNA are duplicated, deleted and sometimes rearranged-in the human population. Genes affected by copy-number variation are good candidates for research into disease susceptibility. The complexity of neuropsychiatric genetics, however, dictates that assessment of the biomedical relevance of copy-number variants and the genes that they affect needs to be considered in an integrated context.

Agreement between fixed observers or methods that produce readings on a continuous scale is usually evaluated via one of several intraclass correlation coefficients (ICCs). This article presents and discusses a few related issues that have not been raised before. ICCs are usually presented in the context of a two-way analysis of variance (ANOVA) model. We argue that the ANOVA model makes inadequate assumptions, such as the homogeneity of the error variances and of the pairwise correlation coefficients between observers. We then present the concept of observer relational agreement which has been used in the social sciences to derive the common ICCs without making the restrictive ANOVA assumptions. This concept did not receive much attention in the biomedical literature. When observer agreement is defined in terms of the difference of the readings of different observers on the same subject (absolute agreement), the corresponding relational agreement coefficient coincides with the concordance correlation coefficient (CCC), which is also an ICC. The CCC, which has gained popularity over the past 15 years, compares the mean squared difference between readings of observers on the same subject with the expected value of this quantity under the assumption of 'chance agreement', which is defined as independence between observers. We argue that the assumption of independence is unrealistic in this context and present a new coefficient that is not based on the concept of chance agreement.

The inflammatory bowel diseases Crohn's disease and ulcerative colitis are common, chronic disorders that cause abdominal pain, diarrhea, and gastrointestinal bleeding. To identify genetic factors that might contribute to these disorders, we performed a genome-wide association study. We found a highly significant association between Crohn's disease and the IL23R gene on chromosome 1p31, which encodes a subunit of the receptor for the proinflammatory cytokine interleukin-23. An uncommon coding variant (rs11209026, c.1142G>A, p.Arg381Gln) confers strong protection against Crohn's disease, and additional noncoding IL23R variants are independently associated. Replication studies confirmed IL23R associations in independent cohorts of patients with Crohn's disease or ulcerative colitis. These results and previous studies on the proinflammatory role of IL-23 prioritize this signaling pathway as a therapeutic target in inflammatory bowel disease.

Background. Few studies have examined the extent to which populations of suicides and attempted suicides are similar, or different. This paper compares suicides and serious suicide attempts in terms of known risk factors for suicidal behaviour. Methods. Using case-control methodology, risk factors for suicidal behaviour were examined in 202 individuals who died by suicide, 275 individuals who made medically serious suicide attempts and 984 randomly selected control subjects. Based on data from significant others, measures used spanned sociodemographic factors, childhood experiences, psychiatric morbidity and psychiatric history, exposure to recent stressful life events and social interaction. Results. Multiple logistic regression identified the following risk factors that were common to suicide and serious suicide attempts: current mood disorder; previous suicide attempts; prior out-patient psychiatric treatment; admission to psychiatric hospital within the previous year; low income; a lack of formal educational qualifications; exposure to recent stressful interpersonal, legal and work-related life events. Suicides and suicide attempts were distinguished in the following ways: suicides were more likely to be male (OR = 1·9, 95% CI 1·1, 3·2); older (OR = 1·03, 95% CI 1·02, 1·04); and to have a current diagnosis of non-affective psychosis (OR = 8·5, 95% CI 2·0, 35·9). Suicide attempts were more likely than suicides to have a current diagnosis of anxiety disorder (OR = 3·5, 95% CI 1·6, 7·8) and to be socially isolated (OR = 2·0, 95% CI 1·2, 3·5). These findings were confirmed by discriminant function analysis, which identified two functions that described the three subject groups: the first function discriminated the two suicide groups from control subjects on a dimension corresponding to risk factors for suicide; the second function discriminated suicide from suicide attempt subjects on a series of factors including gender, non-affective psychosis and anxiety disorder. Conclusions. Suicides and medically serious suicide attempts are two overlapping populations that share common psychiatric diagnostic and history features, but are distinguished by gender and patterning of psychiatric disorder.

Studying the genetics of mood disorders has never been more exciting. We have moved rapidly from establishing the genetic basis of depression to asking questions about how genes are expressed. This has been made possible by the capacity to collect and sequence DNA for large samples cheaply. But "multidisciplinary" approaches investigating interrelationships between risk factors have also been increasingly adopted, encouraging collaborations between those studying genes and those studying the brain, cognition, and/or the social environment. In this review, we first describe findings from quantitative and molecular studies investigating the genetic basis of depression. Second, we present overviews of three hot topics of genetic research: gene-environment interplay, which considers how genetic factors shape exposure and responses toward the social environment; endophenotypic research, which identifies neurophysiological and psychological mediators of genetic risk; and epigenetics, which explain how early environments can foster changes in gene expression, altering subsequent emotional development. Expected final online publication date for the Annual Review of Clinical Psychology Volume 6 is March 27, 2010. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

The graphical presentation of any scientific finding enhances its description, in- terpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixtures of linear structural equations. Second, it provides several examples showing how the method works and how it is implemented and interpreted in practical applications. In particular, this article examines the potentially nonlinear relationships between positive and negative affect and cognitive processing. Third, a recommended dis- play format for illustrating latent bivariate relationships is demonstrated. Finally, the article describes an R package and an online utility that generate these displays automatically.

Genetic and environmental factors, as well as their interactions, are likely to be involved in psychiatric disorders. Considerable progress has been made in association and linkage studies with various candidate genes, at times with conflicting or ambiguous results. An environmental factor that has persistently shown associations with several psychiatric and neurological disorders is the season of birth. If it is the interaction of a specific gene allele with a specific season of birth that constitutes an increased (or decreased) risk for a disorder, then the individuals with this disorder are likely to have a season of birth variation in this gene allele. We investigated the variations in TPH, 5-HTTLPR and DRD4 gene polymorphisms according to seasonality of birth in 954 patients with unipolar affective disorder, bipolar affective disorder, and schizophrenia, respectively, and in 395 controls. We first analyzed season of birth variations in the gene alleles with one cycle or two cycles per year, and then compared specified birth seasons with each other. We found season of birth variations in these gene alleles that were different for different psychiatric disorders. Significant differences between cases and controls could be obtained when restricting the analysis within certain birth seasons but not within others. Our results thus suggest an interaction between the seasons of birth and the expression of the candidate genes, and that season of birth is a confounding variable when investigating the role of the candidate genes in susceptibility to psychiatric disorders.

Risk of complex disorders is thought to be multifactorial, involving interactions between risk factors. However, many genetic studies assess association between disease status and markers one single-nucleotide polymorphism (SNP) at a time, due to the high-dimensional nature of the search space of all possible interactions. Three ensemble methods have been recently proposed for use in high-dimensional data (Monte Carlo logic regression, random forests, and generalized boosted regression). An intuitive way to detect an association between genetic markers and disease status is to use variable importance measures, even though the stability of these measures in the context of a whole-genome association study is unknown. For the simulated data of Problem 3 in the Genetic Analysis Workshop 15 (GAW15), we examined the variability of both rankings and magnitude of variable importance measures using 10 variables simulated to participate in gene x gene and gene x environment interactions. We conducted 500 analyses per method on one randomly selected replicate, tallying the rankings and importance measures for each of the 10 variables of interest. When the simulated effect size was strong, all three methods showed stable rankings and estimates of variable importance. However, under conditions more commonly expected to be encountered in complex diseases, random forests and generalized boosted regression showed more stable estimates of variable importance and variable rankings. Individuals endeavoring to apply statistical learning methods to detect interaction in complex disease studies should perform repeated analyses in order to assure variable importance measures and rankings do not vary greatly, even for statistical learning algorithms that are thought to be stable.

Very few studies have documented relations between personality traits and quality of life among individuals living with human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS). Some have shown that poor perceived quality of life as determined by a sense of purpose may be associated with inadequate adherence to highly active antiretroviral treatment (HAART) in this population. Although adequate HAART adherence is critical to achieve the full therapeutic effects of newly and highly effective regimens, very little is known of how both personality factors and HIV-specific quality of life may impact adherence to these medication regimens. This study evaluated relations among personality traits, quality of life and HAART adherence among 116 men and women living with HIV/AIDS. Results showed that personality traits such as neuroticism were significantly associated with poorer quality of life, whereas conscientiousness and extraversion were associated with better quality of life. In contrast, personality traits were not directly related to HAART adherence. Both higher overall functioning and lower medication worries scores were significantly associated with HAART adherence. Findings suggest that personality traits are associated with HIV-specific quality of life on the one hand, and that HIV-specific quality of life is related to HAART adherence on the other. Future studies assessing the efficacy of psychosocial interventions in improving quality of life and HAART adherence should consider the role of personality traits in promoting better quality of life.

A series of usability tests was run on two enterprise software applications, followed by verbal administration of the System Usability Scale. The original instrument with its 5-point Likert items was presented, as well as an alternate version modified with 7-point Likert items. Participants in the 5-point scale condition were more likely than those presented with the 7-point scale to interpolate, i.e., attempt a response between two discrete values presented to them. In an applied setting, this implied that electronic radio-button style survey tools using 5-point items might not be accurately measuring participant responses. This finding supported the conclusion that 7-point Likert items provide a more accurate measure of a participant's true evaluation and are more appropriate for electronically-distributed and otherwise unsupervised usability questionnaires.

Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal noise, however, the advantage of the GLM over the LM decreases and GLM is no more accurate than LM. We then introduce the Generalized Additive Model (GAM), an extension of GLM that can be used to estimate smooth classification images adaptively. We show that this approach is more robust to the presence of internal noise, and finally, we demonstrate that GAM is readily adapted to estimation of higher order (nonlinear) classification images and to testing their significance.

Although intraclass correlation coefficients (ICCs) are commonly used in behavioral measurement, psychometrics, and behavioral genetics, procedures available for forming inferences about ICCs are not widely known. Following a review of the distinction between various forms of the ICC, this article presents procedures available for calculating confidence intervals and con- ducting tests on ICCs developed using data from one-way and two-way ran- dom and mixed-effect analysis of variance models.

In assessments of attitudes, personality, and psychopathology, unidimensional scale scores are commonly obtained from Likert scale items to make inferences about individuals' trait levels. This study approached the issue of how best to combine Likert scale items to estimate test scores from the practitioner's perspective: Does it really matter which method is used to estimate a trait? Analyses of 3 data sets indicated that commonly used methods could be classified into 2 groups: methods that explicitly take account of the ordered categorical item distributions (i.e., partial credit and graded response models of item response theory, factor analysis using an asymptotically distribution-free estimator) and methods that do not distinguish Likert-type items from continuously distributed items (i.e., total score, principal component analysis, maximum-likelihood factor analysis). Differences in trait estimates were found to be trivial within each group. Yet the results suggested that inferences about individuals' trait levels differ considerably between the 2 groups. One should therefore choose a method that explicitly takes account of item distributions in estimating unidimensional traits from ordered categorical response formats. Consequences of violating distributional assumptions were discussed.

Structural equation modeling (SEM) provides a flexible tool to carry out genetic analyses of family and twin data. The basic model which decomposes the variance between and within families for a particular trait into genetic and non-genetic components can be generalized to multivariate and/or longitudinal data, incorporate sex differences in parameter estimates, and model the effects of measured environment, candidate genes or DNA marker data. We introduce a web-based library (http://www.psy.vu.nl/mxbib) of scripts for uni- and multivariate genetic epidemiological analyses, as well as for linkage and genetic association tests. The scripts are written to be used with the freely available software package Mx and provide a flexible and uniform approach to the analysis of data from relatives.

During the last decade text mining has become a widely used discipline utilizing sta- tistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facili- ties in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

When items exhibit multidimensionality, the dimension along which the collection of items maximally discriminates is the dimension of best measurement. Foundational theoretical results regarding compensatory multidimensionality and the dimension of best measurement are reviewed and served to motivate the current investigations. Key factors of multidimensional data and their influences on the dimension of best measurement are presented and discussed. A theoretical study and a simulation study illustrates these results and investigates the relationship between the dimension of best measurement and the dimension that is estimated when a unidimensional model is fit to the data. The results provide evidence in support of hypotheses regarding (a) factors influencing the dimension of best measurement and (b) the relationship between the dimension of best measurement and the dimension resulting from fitting a unidimensional model. Discussions of implications for practice and future development conclude the paper.

When measuring participant-reported attitudes and outcomes in the behavioral sciences, there are many instances when the common measurement assumption of unidimensionality does not hold. In these cases, the application of a multidimensional measurement model is both technically appropriate and potentially advantageous in substance. In this paper, we illustrate the usefulness of a multidimensional approach to measurement using an empirical example taken from the Behavior Change Consortium. Data from the Treatment Self-Regulation Questionnaire have been analyzed to investigate whether self-regulation can be regarded as a single construct, or if it has multiple dimensions based on the type of regulation or motivation that participants say helps them consider an improvement in healthy behavior. Comparison with consecutive analyses shows the advantages of multidimensional measurement for interpreting participant-reported data.

Both major depressive disorder and bipolar disorder are the subject of a voluminous imaging and genetics literature. Here, we attempt a comprehensive review of MRI and metabolic PET studies conducted to date on these two disorders, and interpret our findings from the perspective of developmental and degenerative models of illness. Elevated activity and volume loss of the hippocampus, orbital and ventral prefrontal cortex are recurrent themes in the literature. In contrast, dorsal aspects of the PFC tend to display hypometabolism. Ventriculomegaly and white matter hyperintensities are intimately associated with depression in elderly populations and likely have a vascular origin. Important confounding influences are medication, phenotypic and genetic heterogeneity, and technological limitations. We suggest that environmental stress and genetic risk variants interact with each other in a complex manner to alter neural circuitry and precipitate illness. Imaging genetic approaches hold out promise for advancing our understanding of affective illness.

Amplifications and deletions of chromosomal DNA, as well as copy-neutral loss of heterozygosity have been associated with diseases processes. High-throughput single nucleotide polymorphism (SNP) arrays are useful for making genome- wide estimates of copy number and genotype calls. Because neighboring SNPs in high throughput SNP arrays are likely to have dependent copy number and genotype due to the underlying haplotype structure and linkage disequilibrium, hidden Markov models (HMM) may be useful for improving genotype calls and copy number estimates that do not incorporate information from nearby SNPs. We improve previous approaches that utilize a HMM framework for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding confidence scores when available. Using simulated data, we demonstrate how confidence scores control smoothing in a probabilistic frame- work. Software for fitting HMMs to SNP array data is available in the R package ICE.

Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation.

This supplement contains extended versions of a selected subset of papers presented at the workshop MLSB 2007, Machine Learning in Systems Biology, Evry, France, from September 24 to 25, 2007.

We used a simulation study to evaluate six approaches for behavior genetic analyses of psychiatric symptom scores. For the selection of the correct model, the best results were obtained with approaches using transformed scores in combination with a procedure involving p-values. With normalizing transformations, the chi 2 test statistic gave a reasonable impression of the overall fit of the model but was less accurate when used as a difference test. The asymptotic distribution free estimation methods yielded chi 2s that were much too large. All data analysis techniques yielded substantially biased parameter estimates. The most biased results were obtained with normalizing transformations. The least biased results were obtained with tobit correlations, but because of its large standard errors the most precise estimates were obtained with polychoric correlations and optimal scale scores. An empirical study showed that a recognition of the role of methodological factors was helpful to understand part of the differences between assessment instruments, raters, and data analysis techniques that were found in the real data.

Cluster and set-cover algorithms are developed to obtain a set of tag single nucleotide polymorphisms (SNPs) that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2>C with at least one tag SNP, where C is specified by the user. AVAILABILITY: http://hkumath.hku.hk/web/link/CLUSTAG/CLUSTAG.html CONTACT: mng@maths.hku.hk.

In this short report, we address some practical problems in performing likelihood-based allelic association analysis of case-control data. Model-free statistics are proposed and their properties assessed by simulation, and procedures based on permutation tests are described for marker-marker as well as marker-disease associations. A memory-efficient algorithm is developed which enables several highly polymorphic markers to be analysed.

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.

BACKGROUND: The primary aim of this study is to examine prospectively the association of stressful life events, social support, depressive symptoms, anger, serum cortisol and lymphocyte subsets with changes in multiple measures of human immunodeficiency virus (HIV) disease progression. METHODS: Ninety-six HIV-infected gay men without symptoms or anti-retroviral medication use at baseline were studied every 6 months for up to 9 years. Disease progression was defined in three ways using the Centers for Disease Control (CDC) classifications (e.g. AIDS, clinical AIDS condition and mortality). Cox regression models with time-dependent covariates were used, adjusting for control variables (e.g. race, age, baseline, CD4 T cells and viral load, number of anti-retroviral medications). RESULTS: Higher cumulative average stressful life events and lower cumulative average social support predicted faster progression to both the CDC AIDS classification and a clinical AIDS condition. Higher anger scores and CD8 T cells were associated with faster progression to AIDS, and depressive symptoms were associated with faster development of an AIDS clinical condition. Higher levels of serum cortisol predicted all three measures of disease progression. CONCLUSIONS: These results suggest that stressful life events, dysphoric mood and limited social support are associated with more rapid clinical progression in HIV infection, with serum cortisol also exerting an independent effect on disease progression.

Single and multiple ratings of test items have become a stock component of standardized ed- ucational tests and surveys. For both formative and summative evaluation of raters, a number of multiple-read rating designs are now commonplace (Wilson and Hoskens, 1999), including designs with as many as six raters per item (e.g. Sykes, Heidorn and Lee, 1999). As digital image based distributed rating becomes commonplace, we expect the use of multiple raters as a routine part of test scoring to grow; increasing the number of raters also raises the possibility of improving the precision of examinee proficiency estimates. In this paper we develop Patz's (1996) hierarchical rater model (HRM) for polytomously scored item response data, and show how it can be used, for example, to scale examinees and items, to model aspects of consen- sus among raters, and to model individual rater severity and consistency effects. The HRM treats examinee responses to open-ended items as unobserved discrete variables, and it explic- itly models the “proficiency” of raters in assigning accurate scores as well as the proficiency of examinees in providing correct responses. We show how the HRM “fits in” to the generaliz- ability theory framework that has been the traditional analysis tool for rated item response data, and give some relationships between the HRM, the design effects correction of Bock, Brennan and Muraki (1999), and the rater bundles model of Wilson and Hoskens (1999). Using simu- lated data, we compare analyses using the conventional IRT Facets model for rating data (e.g. Linacre, 1989; Engelhard, 1994, 1996) and illustrate parameter recovery for the HRM. We also analyze data from a study of three different rating modalities intended to support a Grade 5 mathematics exam given in the State of Florida (Sykes, Heidorn and Lee, 1999) to show how the HRM can be used to identify individual raters of poor reliability or excessive severity, how standard errors of estimation of examinee scale scores are affected by multiple reads, and how the HRM scales up to rating designs involving large numbers of raters.

We make theoretical comparisons among five coefficients-Cronbach's [alpha], Revelle's [beta], McDonald's [omega][sub h], and two alternative conceptualizations of reliability. Though many end users and psychometricians alike may not distinguish among these five coefficients, we demonstrate formally their nonequivalence. Specifically, whereas there are conditions under which [alpha], [beta], and [omega][sub h] are equivalent to each other and to one of the two conceptualizations of reliability considered here, we show that equality with this conceptualization of reliability and between [alpha] and [omega][sub h] holds only under a highly restrictive set of conditions and that the conditions under which [beta] equals [omega][sub h] are only somewhat more general. The nonequivalence of [alpha], [beta], and [omega][sub h] suggests that important information about the psychometric properties of a scale may be missing when scale developers and users only report [alpha] as is almost always the case.

OBJECTIVE-To determine if inadequate approaches to randomized controlled trial design and execution are associated with evidence of bias in estimating treatment effects. DESIGN-An observational study in which we assessed the methodological quality of 250 controlled trials from 33 meta-analyses and then analyzed, using multiple logistic regression models, the associations between those assessments and estimated treatment effects. DATA SOURCES-Meta-analyses from the Cochrane Pregnancy and Childbirth Database. MAIN OUTCOME MEASURES-The associations between estimates of treatment effects and inadequate allocation concealment, exclusions after randomization, and lack of double-blinding. RESULTS-Compared with trials in which authors reported adequately concealed treatment allocation, trials in which concealment was either inadequate or unclear (did not report or incompletely reported a concealment approach) yielded larger estimates of treatment effects (P < .001). Odds ratios were exaggerated by 41% for inadequately concealed trials and by 30% for unclearly concealed trials (adjusted for other aspects of quality). Trials in which participants had been excluded after randomization did not yield larger estimates of effects, but that lack of association may be due to incomplete reporting. Trials that were not double-blind also yielded larger estimates of effects (P = .01), with odds ratios being exaggerated by 17%. CONCLUSIONS-This study provides empirical evidence that inadequate methodological approaches in controlled trials, particularly those representing poor allocation concealment, are associated with bias. Readers of trial reports should be wary of these pitfalls, and investigators must improve their design, execution, and reporting of trials.

OBJECTIVE: To describe alcohol disorders in the general Canadian population, using as a standard indicator the CAGE questionnaire (Have you felt you needed to cut down on your drinking? Have you felt annoyed by criticism of your drinking? Have you felt guilty about drinking? Have you felt you needed a drink first thing in the morning [eye-opener]?). DESIGN: Secondary analysis of data from Canada's Alcohol and Other Drugs Survey (CADS), a national telephone survey conducted in 1994 of a representative sample of 12,155 people aged 15 years or more. PARTICIPANTS: The CAGE questionnaire was administered to 5894 drinkers who had consumed alcohol in the 12 months before the CADS survey. MAIN OUTCOME MEASURES: Respondents with positive (2 or more affirmative responses) and negative results on the CAGE questionnaire were compared as to demographic characteristics, alcohol consumption and harmful consequences of their drinking. Independent predictors of a positive result were identified by means of logistic regression analysis. RESULTS: A total of 5.8% of CAGE-tested current drinkers had a positive result on the past-year CAGE in 1994. The proportion of respondents reporting alcohol-related problems in one or more areas of their life was 7 times greater among drinkers with a positive result on the CAGE questionnaire than among those with a negative result (66.8% v. 9.5%) (p < 0.0001). When all demographic characteristics were controlled for simultaneously, male sex, residence in the Atlantic provinces, Quebec or the Prairies, single/never married or divorced/separated marital status, and low education level were found to be independent risk factors for a positive result on the CAGE questionnaire. About 85% of the respondents with a positive result had not sought help for their drinking. Applying the estimated sensitivity and specificity of the CAGE questionnaire in detecting alcohol dependence, as per criteria of the Diagnostic and Statistical Manual, in a general US population, the authors estimated that 4.1% of Canadians had an alcohol dependence in 1994. CONCLUSION: The large proportion of current drinkers with a positive result on the CAGE questionnaire who did not seek help for their drinking underscores the need for identification and brief interventions by physicians. Further research is needed to elucidate the underlying reasons for regional differences in CAGE status.

The effect of outliers on reaction time analyses is evaluated. The first section assesses the power of different methods of minimizing the effect of outliers on analysis of variance (ANOVA) and makes recommendations about the use of transformations and cutoffs. The second section examines the effect of outliers and cutoffs on different measures of location, spread, and shape and concludes using quantitative examples that robust measures are much less affected by outliers and cutoffs than measures based on moments. The third section examines fitting explicit distribution functions as a way of recovering means and standard deviations and concludes that unless fitting the distribution function is used as a model of distribution shape, the method is probably not worth routine use.

PURPOSE: The Patient-Reported Outcomes Measurement Information System (PROMIS) aims to develop self-reported item banks for clinical research. The PROMIS pediatrics (aged 8-17) project focuses on the development of item banks across several health domains (physical function, pain, fatigue, emotional distress, social role relationships, and asthma symptoms). The psychometric properties of the anxiety and depressive symptom item banks are described. METHODS: Participants (n = 1,529) were recruited in public school settings, hospital-based outpatient and subspecialty pediatrics clinics. The anxiety (k = 18) and depressive symptoms (k = 21) items were split between two test administration forms. Hierarchical confirmatory factor-analytic models (CFA) were conducted to evaluate scale dimensionality and local dependence. IRT analyses were then used to finalize item banks and short forms. RESULTS: CFA results confirmed that anxiety and depressive symptoms are separate constructs and indicative of negative affect. Items with local dependence and DIF were removed resulting in 15 anxiety and 14 depressive symptoms items. The psychometric differences between short forms and simulated computer adaptive tests are presented. CONCLUSIONS: PROMIS pediatric item banks were developed to provide efficient assessment of health-related quality of life domains. This sample provides initial calibrations of anxiety and depressive symptoms item banks and creates PROMIS pediatric instruments, version 1.0.

Comorbidity, the co-occurrence of disorders, is frequently observed to occur at higher rates in clinically ascertained samples than in population-based samples. An explanation for this finding is that subjects suffering from multiple illnesses are more likely to seek medical care and receive a diagnostic evaluation. We refer to the component of the comorbidity between illnesses due to such ascertainment bias as "spurious comorbidity." When spurious comorbidity is present, an apparent association between a candidate locus and the phenotype of interest may actually be attributable to an association between the locus and a comorbid phenotype. This phenomenon, which we call "spurious comorbidity bias," could thus produce misleading association findings. In this article, we describe this phenomenon and demonstrate that it may produce marked bias in the conclusions of family-based association studies. Because of the extremely high rates of comorbidity among psychiatric disorders in clinical samples, this problem may be particularly salient for genetic studies of neuropsychiatric disorders. We conclude that ascertainment bias may contribute to the frequent difficulty in replicating candidate gene study findings in psychiatry. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 96:817-822, 2000.

OBJECTIVE: To examine the development of reading skills among very-low-birthweight (VLBW) children and to what extent reading difficulties at 9 years of age persist unchanged, are attenuated, or are enhanced at 15 years of age. METHODS: Fifty-six VLBW and 52 normal birthweight (NBW) children were assessed on word decoding, word recognition, and reading comprehension at 9 and 15 years of age. RESULTS: VLBW children showed deficits in reading skill at 9 years of age, while most differences obtained at 15 years of age did not reach significance. VLBW children improved their reading comprehension between 9 and 15 years of age more than NBW children, and when controlling for individual differences in IQ, VLBW children improved both their reading comprehension and word-recognition skill. CONCLUSION: The results suggest that VLBW children display positive changes over time in reading skills.

This personal historical article traces the development of the Big-Five factor structure, whose growing acceptance by personality researchers has profoundly influenced the scientific study of individual differences. The roots of this taxonomy lie in the lexical hypothesis and the insights of Sir Francis Galton, the prescience of L. L. Thurstone, the legacy of Raymond B. Cattell, and the seminal analyses of Tupes and Christal. Paradoxically, the present popularity of this model owes much to its many critics, each of whom tried to replace it, but failed. In reaction, there have been a number of attempts to assimilate other models into the five-factor structure. Lately, some practical implications of the emerging consensus can be seen in such contexts as personnel selection and classification.

BACKGROUND: This article systematically monitors the quality of life (QOL) of patients with schizophrenia from seven different sites across four European countries: France, Ireland, Portugal and Spain. METHODS: A one-year prospective cohort study was carried out. Inclusion criteria for patients were: a clinical lifetime diagnosis of schizophrenia according to ICD-10 (F20) diagnostic criteria for research, age between 18 and 65 years and at least one contact with mental health services in 1993. Data concerning QOL were recorded in seven sites from four countries: France, Portugal, Ireland and Spain, and were obtained using the Baker and Intagliata scale. At baseline, 339 patients answered the QOL questionnaire. At one-year follow-up, Spain could not participate, so only 263 patients were contacted and 219 agreed to take part. QOL was compared across centres by areas and according to a global index. QOL was correlated with presence of clinical and social problems, needs for care and interventions provided during the one-year follow-up. RESULTS: We did not find any link between gender and QOL. There were some significant differences between centres concerning many items. What is more, these differences were relative: in Lisbon where the lowest level of satisfaction was recorded, people were satisfied with food but highly dissatisfied with finances, whereas in St Etienne, where the highest level of satisfaction was recorded, people were less satisfied with food when they were more satisfied with finances. The evolution in one year among those respondents who took part in the follow-up (excluding the subjects from Granada) showed different patterns depending on the items. CONCLUSION: The four countries have different resources and patients live in rather different conditions. However, the main differences as far as their QOL is concerned very much depend on extra-psychiatric variables, principally marital status and income.

A logistic regression model may be used to provide predictions of outcome for individual patients at another centre than where the model was developed. When empirical data are available from this centre, the validity of predictions can be assessed by comparing observed outcomes and predicted probabilities. Subsequently, the model may be updated to improve predictions for future patients. As an example, we analysed 30-day mortality after acute myocardial infarction in a large data set (GUSTO-I, n = 40 830). We validated and updated a previously published model from another study (TIMI-II, n = 3339) in validation samples ranging from small (200 patients, 14 deaths) to large (10,000 patients, 700 deaths). Updated models were tested on independent patients. Updating methods included re-calibration (re-estimation of the intercept or slope of the linear predictor) and more structural model revisions (re-estimation of some or all regression coefficients, model extension with more predictors). We applied heuristic shrinkage approaches in the model revision methods, such that regression coefficients were shrunken towards their re-calibrated values. Parsimonious updating methods were found preferable to more extensive model revisions, which should only be attempted with relatively large validation samples in combination with shrinkage.

The eating disorders anorexia and bulimia nervosa have traditionally been regarded as entirely separate from obesity. Eating disorders have been regarded as Western culture-bound syndromes, arising in societies with excessive emphasis on weight, shape and appearance, and best treated by psychological therapies, in particular cognitive behavioural therapy or family-based interventions. In contrast, obesity has been considered a medical illness with metabolic and genetic origins, and thought to be best treated by mainstream medicine, involving dietary, drug or surgical treatment. We believe that this polarisation is fundamentally flawed, and research and treatment of both types of disorder would be better served by greater appreciation of the psychosocial components of obesity and the biological and genetic components of eating disorders. There are similarities in phenotype (such as excessive attempts at weight control, binge eating behaviours) and in risk factors (such as low self-esteem, external locus of control, childhood abuse and neglect, dieting, media exposure, body image dissatisfaction, weight-related teasing and shared susceptibility genes). One example of shared genetic risk is the brain-derived neurotrophic factor (BNDF) gene, in which the valine allele of the Val66Met amino acid polymorphism predisposes to obesity, whereas the methionine allele predisposes to eating disorders. Thus the evidence suggests that these disorders will have both shared and distinct susceptibility factors; some will predispose to both types of disorder, some will push in opposite directions, and some will separate them.

This article focuses on six questions raised by genetic testing in human: (1) the use of genetic tests, (2) information given to relatives of patients affected with genetic disorders, (3) prenatal and preimplantatory diagnosis for late onset genetic diseases and the use of pangenomic tests in prenatal diagnosis, (4) direct-to-consumer genetic testing, (5) population screening in the age of genomic medicine and (6) incidental findings when genetic testing are used.

This paper discusses three open problems in nonparametric polytomous item re- sponse theory: (1) theoretically, the latent trait $θ$ is not stochastically ordered by the observed total score X+; (2) the models do not imply an invariant item ordering; and (3) the regression of an item score on the total score X+ or on the restscore R is not a monotone nondecreasing function and, as a result, it cannot be used for investigating the monotonicity of the item step response function. Tentative solutions for these problems are discussed. The computer program MSP for nonparametric IRT analysis is based on models which neither imply the stochastic ordering property nor an invariant item ordering. Also, MSP uses item-restscore regression for investigating item step response functions. It is discussed whether computer programs may be based temporarily) on models which lack desirable properties and use methods which are not (yet) supported by sound psychometric theory.

ABSTRACT : The HLA region is considered to be the main genetic risk factor for rheumatoid arthritis. Previous research demonstrated that HLA-DRB1 alleles encoding the shared epitope are specific for disease that is characterized by antibodies to cyclic citrullinated peptides (anti-CCP). In the present study, we incorporated the shared epitope and either anti-CCP antibodies or rheumatoid factor into linkage disequilibrium mapping, to assess the association between the shared epitope or antibodies with the disease gene identified. Incorporating the covariates into the association mapping provides a mechanism 1) to evaluate gene-gene and gene-environment interactions and 2) to dissect the pathways underlying disease induction/progress in quantitative antibodies.

Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping-the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the National Institutes of Health Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.

Impulsive unsocialized sensation seeking (ImpUSS) is a major factor discovered in factor analyses of scales used in psychobiological research. It is strongly convergent with Eysenck's P dimension and conscientiousness in the 'big five'. The components of the dimension and the P scale, have been validated as correlates of various kinds of disinhibited behaviors, criminality, sexuality, and substance use and abuse. ImpUSS is related to a failure in passive avoidance learning, probably as a function of impulsivity and attention to reward stimuli. Psychophysiological markers for the trait include strong orienting and weak defensive reflexes and an augmenting, rather than reducing, of cortical reaction to intense stimuli. At the neurochemical level the trait is related to low levels of monoamine oxidase (MAO) and the neurotransmitters norepinephrine and serotonin, and theoretically high levels of dopaminergic activity. The trait components have high heritabilities for a personality trait.

Depression and impaired quality of life (QOL) are frequently observed in patients suffering from a variety of diseases. In addition, it has been reported that an enhanced degradation of the serotonin precursor tryptophan may contribute to QOL deterioration in some diseases. However, it is unclear whether the correlation between the QOL scores and the central serotonergic tone is only mediated by the severity of either the depression symptoms or the physical illness itself. The present study examined the relationship between serotonin transporter (SERT) availability and life quality as measured by the World Health Organization Quality of Life brief version questionnaire (WHO-QOL) in healthy participants in order to exclude the influence of depressive mood and disease. The SERT availability in the midbrain was approximated using SPECT with [(123)I] ADAM ligand in fifty-eight healthy volunteers. The overall rating sub scores of the WHO-QOL correlated positively with serotonin transporter availability in the males. Central serotoninergic activity may play a role in the overall rating scores of the WHO-QOL.

Alzheimer's disease (AD) is a complex disease process, so finding a single biomarker to track in clinical trials has proven difficult. This paper describes and contrasts statistical methods that might be used with biomarkers in clinical trials for AD, highlighting their differences, limitations and interpretations. The first method is traditional regression, within which one dependent variable, the Best Empirically Supported Indicator (BESI), must be identified. In this approach one biomarker (e.g., the ratio of tau to Abeta42 from CSF) is the indicator for an individual's disease status, and change in that status. The second approach is an exploratory factor analysis (EFA) to consolidate a multitude of candidate dependent variables into a sample-dependent, mathematically-optimized smaller set of 'factors'. The third method is latent variable (LV) modeling of multiple indicators of an entity (e.g., "disease burden"). The LV approach can yield a complex 'dependent variable', the Best Measurement Model Indicator (BMMI). A measurement model represents an entity that several dependent variables reflect or measure, and so can include many 'dependent variables', and estimate their relative contributions to the underlying entity. The selection of a single BESI is an artifact of regression that limits the investigator's ability to utilize all relevant variables representing the entity of interest. EFA results in sample-specific combination of biomarkers that might not generalize to a new sample - and fit of the EFA results cannot be tested. Latent variable methods can be useful to construct powerful, efficient statistical models that optimally combine diverse biomarkers into a single, multidimensional dependent variable that can generalize across samples when they are theory-driven and not sample-dependent. This paper shows that EFA can work to uncover underlying structure, but that it does not always yield solutions that 'fit' the data. It is not recommended as a method to build BMMIs, which will be useful in establishing diagnostic criteria, creating and evaluating benchmarks, and monitoring progression in clinical trials.

The emergence of client-focused research has led to substantial change in the field of outcome assessment. Using the client's clinical characteristics to determine expected outcome, client monitoring assists clinicians in assessing the current treatment plan and client progress. The methodology has been shown to be effec- tive in outpatient samples, by implementing change in real time for the benefit of the individual (Lambert, Harmon, Slade, Whipple, & Hawkins, 2005). Accordingly, the framework is an appropriate and important addition to the assessment of clinical practice.

This article studies recalled parental bonding, adult attachment style, and personality disorders in child molesters and reports on the findings of two separate studies. The first study examines differences between a group of 84 child molesters and 80 matched normal control subjects. This study found that the antisocial and the schizoid personality disorders are typical for the molester group, and that at an interpersonal level this group can be typified by recollections of an uncaring father and mother, recollections of an elevated level of autonomy emanating from the father, and insecure current attachment patterns. The second study compares a subgroup of personality-disordered child molesters to a subgroup without personality disorders. This study revealed that recollections of the role of the father in parenting are decisive. The personality-disordered group reports that the father was both more uncaring and granted more autonomy. Regarding current adult attachment style, an avoidant and anxious-ambivalent attachment style characterised the disordered subgroup. We argue that the results can be useful for treatment. Since recalled parental experiences play a role in the development of personality disorders and child molestation, psychotherapists should integrate interpersonal tools into treatment, especially in therapeutic work with child molesters who received less parental sensitivity and suffer from personality disorders.

Adolescence is the transition period that prepares individuals for fulfilling their role as adults. Most conspicuous in this transition period is the peak level of risk-taking behaviors that characterize adolescent motivated behavior. Significant neural remodeling contributes to this change. This review focuses on the functional neuroanatomy underlying motivated behavior, and how ontogenic changes can explain the typical behavioral patterns in adolescence. To help model these changes and provide testable hypotheses, a neural systems-based theory is presented. In short, the Triadic Model proposes that motivated behavior is governed by a carefully orchestrated articulation among three systems, approach, avoidance and regulatory. These three systems map to distinct, but overlapping, neural circuits, whose representatives are the striatum, the amygdala and the medial prefrontal cortex. Each of these system-representatives will be described from a functional anatomy perspective that includes a review of their connectivity and what is known of their ontogenic changes.

BACKGROUND: Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes). METHODOLOGY/PRINCIPAL FINDINGS: We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes. CONCLUSIONS/SIGNIFICANCE: We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development.

This paper presents a statistical approach to collaborative filtering and investigates the use of latent class models for predicting individ- ual choices and preferences based on observed preference behavior. Two models are discussed and compared: the aspect model, a proba- bilistic latent space model which models indi- vidual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algo- rithms for different variants of the aspect model and derive an approximate EM algorithm based on a variational principle for the two-sided clus- tering model. The benefits of the different mod- els are experimentally investigated on a large movie data set.

BACKGROUND: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-throughput genomic data and construct appropriate classification rules. It is also of interest to develop methods for evaluation of classification performance and ranking of identified biomarkers. RESULTS: The ROC (receiver operating characteristic) technique has been widely used in disease classification with low dimensional biomarkers. Compared with the empirical ROC approach, the binormal ROC is computationally more affordable and robust in small sample size cases. We propose using the binormal AUC (area under the ROC curve) as the objective function for two-sample classification, and the scaled threshold gradient directed regularization method for regularized estimation and biomarker selection. Tuning parameter selection is based on V-fold cross validation. We develop Monte Carlo based methods for evaluating the stability of individual biomarkers and overall prediction performance. Extensive simulation studies show that the proposed approach can generate parsimonious models with excellent classification and prediction performance, under most simulated scenarios including model mis-specification. Application of the method to two cancer studies shows that the identified genes are reasonably stable with satisfactory prediction performance and biologically sound implications. The overall classification performance is satisfactory, with small classification errors and large AUCs. CONCLUSION: In comparison to existing methods, the proposed approach is computationally more affordable without losing the optimality possessed by the standard ROC method.

There is growing recognition that patient-reported outcome (PRO) measures-encompassing, for example, health-related quality of life-can complement traditional biomedical outcome measures (eg, survival, disease-free survival) in conveying important information for cancer care decision making. This paper provides an integrated review and interpretation of how PROs have been defined, measured, and used in a range of recent cancer research and policy initiatives. We focus, in turn, on the role of PRO measurement in the evaluation and approval of cancer therapies, the assessment of cancer care in the community, patient-provider decision making in clinical oncology practice, and population surveillance of cancer patients and survivors. The paper concludes with a discussion of future challenges and opportunities in PRO measure development and application, given the advancing state of the science in cancer outcomes measurement and the evolving needs of cancer decision makers at all levels.

Alcohol dependence (AD), a genetically influenced phenotype, is extremely costly to individuals and to society in the United States and throughout the world, contributing to morbidity and mortality and a host of economic, interpersonal, and societal problems. Although until recently the only genes established to affect risk for AD were those encoding several alcohol metabolizing enzymes, there are now several other genes that can be regarded as confirmed risk loci, discovered through linkage and candidate gene association studies. While the mechanism of action of the effects of alcohol-metabolizing enzymes on AD risk is thought to be well understood, we are still in the early stages of understanding the physiology of other risk loci. Further, it is clear that only a small number of the many genes that influence risk for AD have been identified. Newer methodologies (e.g., genomewide association, study of copy number variation, and deep sequencing of candidate loci to identify rare risk variants) that have improved our understanding of other complex traits hold the promise of identifying a greater set of AD susceptibility loci.

Children who have been diagnosed with any one developmental disorder are very likely to meet diagnostic criteria for some other developmental disorder. Although comorbidity has long been acknowledged in childhood disorders, little is understood about the mechanisms that are responsible for the high level of comorbidity. In a series of studies, we have investigated the link between sensory-motor deficits and developmental disorders. Poor sensory-motor integration has long been implicated as a cause of motor problems in developmental disorders such as developmental coordination disorder (DCD), and our recent research has also investigated sensory-motor deficits in children with attention deficit hyperactivity disorder (ADHD) and autistic disorder. Based on a critical examination of relevant literature and some of our recent research findings, we argue that the importance of poor sensory-motor functioning in discriminating children with different disorders has been underestimated. Poor sensory-motor coordination appears to be linked to DCD, but not ADHD. Also, sensory-motor deficits in children with DCD and autistic disorder may provide insight into some of the social difficulties found in these groups of children. This research will increase our understanding of why children with one developmental disorder typically also have problems in other areas.

We introduce a new data mining problem: mining truth tables in binary datasets. Given a matrix of objects and the properties they satisfy, a truth table identifies a subset of properties that exhibit maximal variability (and hence, complete independence) in occurrence patterns over the un- derlying objects. This problem is relevant in many domains, e.g., in bioinformatics where we seek to identify and model independent components of combinatorial regulatory path- ways, and in social/economic demographics where we desire to determine independent behavioral attributes of popula- tions. We outline a family of levelwise approaches adapted to mining truth tables, algorithmic optimizations, and ap- plications to bioinformatics and political datasets.

A SAS macro solution is presented for clustering of high dimensional low sample size (HDLLSS) data using a new algorithm based o p-values as similarity measure. The algorithm PPCLUST was developed by von Borries (2008) and implemented using SAS macro language with the macro autocall facility and window macro command for friendly interface. The SAS interface to JMP was used to run a SAS macro inside JMP and automatically produce graphs using adaptable JMP scripts. An example with partial data from microarray study is presented.

This article discusses the TextMate text editor and its many capabilities that make working with LATEX documents a lot easier. Some of its features in- clude syntax highlighting, various methods for automatic insertion of text (such as the begin-end blocks in environments and automatic labels for sec- tion commands), lookup of labels and cite keys based on partial matches, as well as tools for dealing with large projects. TextMate is designed with the user in mind, so it is easy to customize it to your needs. During its short lifetime (about two and a half years) it has gained many supporters and has become a very popular text editor for the Mac OS X platform, and especially among LATEX users, as can be seen from the exponential growth in the number of users, and the large number of LATEX related questions on the TextMate mailing list. In addition to this article, the first author's weblog can be used as a starting point for learning more about using TextMate for LATEX: http:// skiadas.dcostanet.net/afterthought

The Behrens-Fisher problem concerns the inference for the difference between the means of two normal populations whose ratio of variances is unknown. In this situation, Fisher's fiducial interval differs markedly from the Neyman-Pearson confidence interval. A prior proposed by Jeffreys leads to a credible interval that is equivalent to Fisher's solution, but carries a different interpretation. The authors propose an alternative prior leading to a credible interval whose asymptotic coverage probability matches the frequentist cover- age probability more accurately than Jeffreys' interval. Their simulation results indicate excellent matching even in small samples.

As the patient is the primary recipient of treatment, there is a need to recognize and value the patient's perception of change in response to treatment in clinical trials. A new outcomes classification, patient-reported outcomes (PROs), has been proposed by regulators. The PROs are used as an umbrella term and include, for example measures of subjective symptoms, health-related quality of life (HRQL) and treatment satisfaction. In this sense, the PROs are unique and complementary indicators of disease activity as well as of treatment efficacy. Frequently, pharmaceutical companies desire to include PRO benefits in the product label in order to reach a broad range of customers, including prescribers as well as patients. Therefore such information must be based on results that are scientifically valid. The measurement strategy, i.e. the thinking about and decision-making related to PRO and HRQL evaluations, needs to be explicit for clinical trials. First of all it is necessary to specify and provide the rationale for measuring the PROs. Similarly, the reason for selecting particular instruments should be stated and, for the selected instruments, the psychometric evidence should be summarized. The key PRO domains must be prespecified and evidence of their importance to patients should be provided. The research question under study and potential claims need to be clearly delineated. Hence, instrument selection is a key initial step for planning successful and scientifically adequate clinical trials intended to support labelling and promotional claims of PRO benefits to patients. The scientific criteria and design issues of clinical trials are the same for clinical and PRO endpoints. However, important issues of particular relevance to PRO assessments, such as missing values, multiple outcomes, and the statistical analysis, require careful attention. The thinking and planning involved in developing the PRO component of the clinical trial need to be articulated. Successful evaluation of PROs in clinical trials relies on careful planning provided the treatment shows sufficient effectiveness.

BACKGROUND: Although there is sufficient evidence that the quality of life of people suffering from depression is reduced hardly anything is known about their quality of life after the remission of a depressive episode. AIMS: We therefore set out to study the quality of life of patients with depression (ICD-10 F32, F33) one, four and seven months after discharge from hospital. For comparison, a random sample of the general population was studied in addition. METHOD: Quality of life was assessed by means of the WHOQOL-100, a self-administered questionnaire developed by WHO. RESULTS: Although, shortly after discharge, quality of life of patients whose depression remitted was better than that of patients with persisting depression it was still slightly worse than that of the general population. During the subsequent six months, there was no further improvement of quality of life, i.e. even at the end of the follow-up period there was a slight lack of quality of life, especially as concerns the level of independence, spirituality/religion/personal beliefs and physical health. CONCLUSIONS: Thus, what already had been reported based on the objective assessment of quality of life, namely that depression implies a persisting impairment of social functioning and living conditions, can be replicated to some extent from the point of view of the patients themselves.

In genetic association studies, linkage disequilibrium (LD) within a region can be exploited to select a subset of single-nucleotide polymorphisms (SNPs) to genotype with minimal loss of information. A novel entropy-based method for selecting SNPs is proposed and compared to an existing method based on the coefficient of determination (R2) using simulated data from Genetic Analysis Workshop 14. The effect of the size of the sample used to investigate LD (by estimating haplotype frequencies) and hence select the SNPs is also investigated for both measures. It is found that the novel method and the established method select SNP subsets that do not differ greatly. The entropy-based measure may thus have value because it is easier to compute than R2. Increasing the sample size used to estimate haplotype frequencies improves the predictive power of the subset of SNPs selected. A smaller subset of SNPs chosen using a large initial sample to estimate LD can in some instances be more informative than a larger subset chosen based on poor estimates of LD (using a small initial sample). An initial sample size of 50 individuals is sufficient in most situations investigated, which involved selection from a set of 7 SNPs, although to select a larger number of SNPs, a larger initial sample size may be required.

A debate has emerged as to whether recognition of emotional stimuli is more accurate or more biased than recognition of nonemotional stimuli. Teasing apart changes in accuracy versus changes in bias requires a measurement model. However, different models have been adopted by different researchers, and this has contributed to the current debate. In this article, different measurement models are discussed, and the signal detection model that is most appropriate for recognition is adopted to investigate the effects of valence and arousal on recognition memory performance, using receiver operating characteristic analyses. In addition, complementary two-alternative forced choice experiments were conducted in order to generalize the empirical findings and interpret them under a relatively relaxed set of measurement assumptions. Across all experiments, accuracy was greater for highly valenced stimuli and stimuli with high arousal value. In addition, a bias to endorse positively valenced stimuli was observed. These results are discussed within an adaptive memory framework that assumes that emotion plays an important role in the allocation of attentional resources.

The association between attachment and symbolic play was examined in a sample of 45 preschool age boys with autism spectrum disorders. Attachment was assessed using the strange situation procedure, and the frequency, duration, diversity and complexity of child-initiated symbolic play was assessed from observations of mother-child interactions during free play and doll play. We hypothesized that children with secure attachments will score higher on measures of symbolic play compared to children with insecure attachments, and that children with organized attachments will also score higher on measures of symbolic play compared to children with disorganized attachments. Only the second hypothesis received support, and the reasons for this, as well as the implications of the findings for attachment theory, are discussed.

Autism and related traits are highly heritable but cannot be explained by currently known genetic risk factors. Therefore, the advent of genome-wide single nucleotide polymorphism (SNP) and copy number variant (CNV) microarray technologies heralded identification of additional autism loci. CNVs associated with autism seem to show variable expressivity, also leading to other phenotypes, such as schizophrenia, mental retardation/developmental delay and epilepsy. Initial genome-wide SNP-association studies have each identified a single novel associated locus with modest effect. Based on the lessons from other complex common disease, larger sample sizes and meta-analyses are likely to identify additional SNP loci, and the genes implicated may act on multiple related disorders. Even if common alleles or rare variants hold little predictive value, neurodevelopmental pathways disrupted in autism may be identified. Future research might yet uncover common CNV risk loci and rare single nucleotide risk alleles, which are currently difficult to detect. The genetic architecture and phenotypic heterogeneity identified so far suggest additional approaches, such as population-based research and study of relevant neurobiological endophenotypes.

OBJECTIVE: To apply Rasch measurement to develop a rule for clinical interpretation of the Observer Scar Assessment Scale (OSAS) to help surgeons judge reported sum scores clinically. STUDY DESIGN AND SETTING: We used cross-sectional data of a multicenter randomized clinical trial for the treatment of nontuberculous cervicofacial lymphadenitis in children. Rasch analysis was used on the OSAS scores obtained from scar photographs of 100 children after surgical or antibiotic treatment. RESULTS: Rasch analysis showed that all OSAS item rating scales needed revision and weighting. After doing so, raw scale scores could be converted into quantitative interval scale-based measures of scar quality. The clinical interpretation of the OSAS was clearly improved by the revised scoring. By using the Rasch-modeled item and person measures, the most likely OSAS score patterns associated with the revised OSAS sum scores could be determined. CONCLUSIONS: The Rasch analysis improved the measurement properties and clinical interpretability of the OSAS instrument.

Recent advances of information technology in biomedical sci- ences and other applied areas have created numerous large diverse data sets with a high dimensional feature space, which provide us a tremendous amount of information and new opportunities for improving the quality of human life. Meanwhile, great challenges are also created driven by the continuous arrival of new data that requires researchers to convert these raw data into scientific knowledge in order to benefit from it. Association studies of complex diseases using SNP data have become more and more popular in biomedical research in recent years. In this paper, we present a review of recent statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic association studies for complex dis- eases. The review includes both general feature reduction approaches for high dimensional correlated data and more specific approaches for SNPs data, which include unsupervised haplotype mapping, tag SNP selection, and supervised SNPs selection using statistical testing/scoring, statistical modeling and machine learning methods with an emphasis on how to iden- tify interacting loci.

We studied human population structure using genotypes at 377 autosomal microsatellite loci in 1056 individuals from 52 populations. Within-population differences among individuals account for 93 to 95% of genetic variation; differences among major groups constitute only 3 to 5%. Nevertheless, without using prior information about the origins of individuals, we identified six main genetic clusters, five of which correspond to major geographic regions, and subclusters that often correspond to individual populations. General agreement of genetic and predefined populations suggests that self-reported ancestry can facilitate assessments of epidemiological risks but does not obviate the need to use genetic information in genetic association studies.

BACKGROUND: The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test. RESULTS: It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large. CONCLUSION: Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.

OBJECTIVES: To discover what types of scientific misconduct are most likely to influence the results of a clinical trial. DESIGN: Delphi survey of expert opinion with three rounds of consultation. SETTING: Non-industry clinical trial "community". PARTICIPANTS: Experts identified from invitees to a previous MRC consultation on clinical trials. 32 out of the 40 experts approached agreed to participate. RESULTS: We identified thirteen forms of scientific misconduct for which there was majority agreement (>50%) that they would be likely or very likely to distort the results and majority agreement (>50%) that they would be likely or very likely to occur. Of these, the over-interpretation of 'significant' findings in small trials, selective reporting and inappropriate subgroup analyses were the main themes. CONCLUSIONS: According to this expert group, the most important forms of scientific misconduct in clinical trials are selective reporting and the opportunistic use of the play of chance. Data fabrication and falsification were not rated highly because it was considered that these were unlikely to occur. Registration and publication of detailed clinical trial protocols could make an important contribution to preventing scientific misconduct.

In this rejoinder, we respond to comments by Lynn, Rushton, and Templer on our previous paper in which we criticized the use of national IQs in studies of evolutionary theories of race differences in intelligence. We reiterate that because of the Flynn Effect and psychometric issues, national IQs cannot be taken to reflect populations' levels of g as fixed since the last ice age. We argue that the socio-cultural achieve- ments of peoples of Mesopotamia and Egypt in 3000 B.C. stand in stark contrast to the current low level of national IQ of peoples of Iraq and Egypt and that these ancient achievements appear to contradict evo- lutionary accounts of differences in national IQ. We argue that race differences in brain size, even if these were entirely of genetic origin, leave unexplained 91-95% of the black-white IQ gap. We highlight addi- tional problems with hypotheses raised by Rushton and Templer. National IQs cannot be viewed solely in evolutionary terms but should be considered in light of global differences in socio-economic develop- ment, the causes of which are unknown.

This critical appraisal aims to position the five-factor model within the multifaceted field of personality psychology by highlighting six important limitations of the model. These are the model's (a) inability to address core constructs of personality functioning beyond the level of traits; (b) limitations with respect to the prediction of specific behavior and the adequate description of persons' lives; (c) failure to provide compelling causal explanations for human behavior and experience; (d) disregard of the contextual and conditional nature of human experience; (e) failure to offer an attractive program for studying personality organization and integration; and (f) reliance on simple, noncontingent, and implicitly comparative statements about persons. The five-factor model is essentially a "psychology of the stranger," providing information about persons that one would need to know when one knows nothing else about them. It is argued that because of inherent limitations, the Big Five may be viewed as one important model in personality studies but not the integrative model of personality.

BACKGROUND: Personality traits are considered risk factors for drug use, and, in turn, the psychoactive substances impact individuals' traits. Furthermore, there is increasing interest in developing treatment approaches that match an individual's personality profile. To advance our knowledge of the role of individual differences in drug use, the present study compares the personality profile of tobacco, marijuana, cocaine, and heroin users and non-users using the wide spectrum Five-Factor Model (FFM) of personality in a diverse community sample. METHOD: Participants (N = 1,102; mean age = 57) were part of the Epidemiologic Catchment Area (ECA) program in Baltimore, MD, USA. The sample was drawn from a community with a wide range of socio-economic conditions. Personality traits were assessed with the Revised NEO Personality Inventory (NEO-PI-R), and psychoactive substance use was assessed with systematic interview. RESULTS: Compared to never smokers, current cigarette smokers score lower on Conscientiousness and higher on Neuroticism. Similar, but more extreme, is the profile of cocaine/heroin users, which score very high on Neuroticism, especially Vulnerability, and very low on Conscientiousness, particularly Competence, Achievement-Striving, and Deliberation. By contrast, marijuana users score high on Openness to Experience, average on Neuroticism, but low on Agreeableness and Conscientiousness. CONCLUSION: In addition to confirming high levels of negative affect and impulsive traits, this study highlights the links between drug use and low Conscientiousness. These links provide insight into the etiology of drug use and have implications for public health interventions.

The concept of mediation has broad applications in medical health studies. Although the statistical assessment of a mediational effect under the normal assumption has been well established in linear structural equation models (SEM), it has not been extended to the general case where normality is not a usual assumption. In this paper, we propose to extend the definition of mediational effects through causal inference. The new definition is consistent with that in linear SEM and does not rely on the assumption of normality. Here, we focus our attention on the logistic mediation model, where all variables involved are binary. Three approaches to the estimation of mediational effects-Delta method, bootstrap, and Bayesian modelling via Monte Carlo simulation are investigated. Simulation studies are used to examine the behaviour of the three approaches. Measured by 95 per cent confidence interval (CI) coverage rate and root mean square error (RMSE) criteria, it was found that the Bayesian method using a non-informative prior outperformed both bootstrap and the Delta methods, particularly for small sample sizes. Case studies are presented to demonstrate the application of the proposed method to public health research using a nationally representative database. Extending the proposed method to other types of mediational model and to multiple mediators are also discussed.

This paper aims to provide a non‐technical overview of multidimensional scaling (MDS) so that a broader population of psychologists, in particular, will consider using this statistical procedure. A brief description regarding the type of data used in MDS, its acquisition and analyses via MDS is provided. Also included is a commentary on the unique challenges associated with assessing the output of MDS. Our second aim, by way of discussing representative studies, is to highlight and evaluate the utility of this method in various domains in psychology.

Background The use of structural equation modeling and latent variables remains uncommon in epidemiology despite its potential usefulness. The latter was illustrated by studying cross-sectional and longitudinal relationships between eating behavior and adiposity, using four different indicators of fat mass. Methods Using data from a longitudinal community-based study, we fitted structural equation models including two latent variables (respectively baseline adiposity and adiposity change after 2 years of follow-up), each being defined, by the four following anthropometric measurement (respectively by their changes): body mass index, waist circumference, skinfold thickness and percent body fat. Latent adiposity variables were hypothesized to depend on a cognitive restraint score, calculated from answers to an eating-behavior questionnaire (TFEQ-18), either cross-sectionally or longitudinally. Results We found that high baseline adiposity was associated with a 2-year increase of the cognitive restraint score and no convincing relationship between baseline cognitive restraint and 2-year adiposity change could be established. Conclusions The latent variable modeling approach enabled presentation of synthetic results rather than separate regression models and detailed analysis of the causal effects of interest. In the general population, restrained eating appears to be an adaptive response of subjects prone to gaining weight more than as a risk factor for fat-mass increase.

ABSTRACT: BACKGROUND: Generalised Anxiety Disorder (GAD) is a highly prevalent psychiatric disorder. Effective prevention in young adulthood has the potential to reduce the prevalence of the disorder, to reduce disability and lower the costs of the disorder to the community. The present trial (the WebGAD trial) aims to evaluate the effectiveness of an evidence-based online prevention website for GAD. METHODS: The principal clinical question under investigation is the effectiveness of an online GAD intervention (E-couch) using a community-based sample. We examine whether the effect of the intervention can be maximised by either human support, in the form of telephone calls, or by automated support through emails. The primary outcome will be a reduction in symptoms on the GAD-7 in the active arms relative to the non active intervention arms. DISCUSSION: The WebGAD trial will be the first to evaluate the use of an internet-based cognitive behavioural therapy (CBT) program contrasted with a credible control condition for the prevention of GAD and the first formal RCT evaluation of a web-based program for GAD using community recruitment. In general, internet-based CBT programs have been shown to be effective for the treatment of other anxiety disorders such as Post Traumatic Stress Disorder, Social Phobia, Panic Disorder and stress in clinical trials; however there is no evidence for the use of internet CBT in the prevention of GAD. Given the severe shortage of therapists identified in Australia and overseas, and the low rates of treatment seeking in those with a mental illness, the successful implementation of this protocol has important practical outcomes. If found to be effective, WebGAD will provide those experiencing GAD with an easily accessible, free, evidence-based prevention tool which can be promoted and disseminated immediately. Trial Registration Controlled-trials.com: ISRCTN76298775.

In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.

Based on the demand for new verbal reasoning tests to enrich psychological test inventory, a pilot version of a new test was analysed: the 'Family Relation Reasoning Test' (FRRT; Poinstingl, Kubinger, Skoda & Schechtner, forthcoming), in which several basic cognitive operations (logical rules) have been embedded/implemented. Given family relationships of varying complexity embedded in short stories, testees had to logically conclude the correct relationship between two individuals within a family. Using empirical data, the linear logistic test model (LLTM; Fischer, 1972), a special case of the Rasch model, was used to test the construct validity of the test: The hypothetically assumed basic cognitive operations had to explain the Rasch model's item difficulty parameters. After being shaped in LLTM's matrices of weights ((qij)), none of these operations were corroborated by means of the Ander- sen's Likelihood Ratio Test.

This paper argues that the Rasch model, unlike the other models generally referred to as IRT models, and those that fall into the tradition of True Score models, encompasses a set of rigorous prescriptions for what scientific measurement would be like if it were to be achieved in the social sciences. As a direct consequence, the Rasch measurement approach to the construction and monitoring of variables is sensitive to the issues raised in Messick's (1995) broader conception of construct validity. The theory / practice dialectic (Bond & Fox, 2001) ensures that validity is foremost in the mind of those developing measures and that genuine scientific measurement is foremost in the minds of those who seek valid outcomes from assessment. Failures of invariance, such as those referred to as DIF, should alert researchers to the need to modify assessment procedures or the substantive theory under investigation, or both.

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of approximately 0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.

In this chapter we provide a basic introduction to IRT modeling (item response theory), including a discussion of the common IRT models used in research, underlying assumptions of these models, and differences between CTT and IRT modeling. The introduction is followed by a demonstration of the information that can be gained by using IRT to evaluate the psychometric properties of a questionnaire.

Missing single nucleotide polymorphisms (SNPs) are quite common in ge- netic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease associa- tion. In this article, we compare two haplotype-based imputation approaches and one regression tree-based imputation approach for association studies. The goal is to assess the imputation accuracy, and to evaluate the impact of imputa- tion on parameter estimation. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case-control study on developmental dyslexia. Our results suggest that imputation generally improves over the standard practice of ignoring missing data in terms of bias and efficiency. The haplotype-based approaches slightly outperform the tree-based approach when there are a small number of SNPs in linkage disequilibrium (LD), but the latter has a computa- tional advantage. Finally, we demonstrate that utilizing the disease status in imputation helps to reduce the bias in the subsequent parameter estimation.

OBJECTIVE:: Adult-onset schizophrenia has repeatedly been associated with disturbances in the temporal lobes and alterations in cortical folding, which are thought to reflect neurodevelopmental impairment. Early-onset schizophrenia (EOS; onset before 18 years) is considered to involve even more pronounced neurodevelopmental deviance across a wide range of brain structural measures. We hypothesized that overall alteration of cortical folding also applies to EOS, and EOS involves prominent structural aberrations in superior temporal and collateral sulci. METHOD:: Magnetic resonance T1 images of 51 patients with EOS and 59 healthy participants were investigated. A fully automated method was applied to the images to extract, label, and measure the sulcus area in the whole cortex. Cortical folding was assessed by computing global sulcal indices (the ratio between total sulcal area and total outer cortex area) for each hemisphere and local sulcal indices (the ratio between the area of labeled sulcus and total outer cortex area in the corresponding hemisphere) for superior temporal and collateral sulci. RESULTS:: Relative to healthy individuals, patients with EOS had significantly lower global sulcal indices in both hemispheres and a lower local sulcal index in the left collateral sulcus. CONCLUSIONS:: Reduced hemispheric sulcation appears to be a feature of schizophrenia, irrespective of age at onset. Structural aberration involving the left collateral sulcus may contribute to neurobiological substrate of EOS.

So-called “nonparametric” statistical methods are often in fact based on pop- ulation parameters, which can be estimated (with confidence limits) using the corresponding sample statistics. This article reviews the uses of three such param- eters, namely Kendall's τa, Somers' D and the Hodges-Lehmann median difference. Confidence intervals for these are demonstrated using the somersd package. It is argued that confidence limits for these parameters, and their differences, are more informative than the traditional practice of reporting only p-values. These three parameters are also important in defining other tests and parameters, such as the Wilcoxon test, the area under the receiver operating characteristic (ROC) curve, Harrell's C, and the Theil median slope.

The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for active learning, a summary of several problem setting variants, and a discussion of related topics in machine learning research are also presented.

BACKGROUND AND OBJECTIVES: Most health-related quality-of-life questionnaires include multi-item scales. Scale scores are usually estimated as simple sums of the item scores. However, scoring procedures utilizing more information from the items might improve measurement abilities, and thereby reduce the needed sample sizes. We investigated whether item response theory (IRT)-based scoring improved the measurement abilities of the EORTC QLQ-C30 physical functioning, emotional functioning, and fatigue scales. METHODS: Using a database of 13,010 subjects we estimated the relative validities of IRT scoring compared to sum scoring of the scales. RESULTS: The mean relative validities were 1.04 (physical), 1.03 (emotional), and 0.97 (fatigue). None of these were significantly larger than 1. Thus, no gain in measurement abilities using IRT scoring was found for these scales. Possible explanations include that the items in the scales are not constructed for IRT scoring and that the scales are relatively short. CONCLUSION: IRT scoring of the three longest EORTC QLQ-C30 scales did not improve measurement abilities compared to the traditional sum scoring of the scales.

ABSTRACT : While genetic and environmental factors and their interactions influence susceptibility to rheumatoid arthritis (RA), causative genetic variants have not been identified. The purpose of the present study was to assess the effects of covariates and genotype x sex interactions on the genome-wide association analysis (GWAA) of RA using Genetic Analysis Workshop 16 Problem 1 data and a logistic regression approach as implemented in PLINK. After accounting for the effects of population stratification, effects of covariates and genotype x sex interactions on the GWAA of RA were assessed by conducting association and interaction analyses. We found significant allelic associations, covariate, and genotype x sex interaction effects on RA. Several top single-nucleotide polymorphisms (SNPs) ( 22 SNPs) showed significant associations with strong p-values (p < 1 x 10-4 - p < 1 x 10-24). Only three SNPs on chromosomes 4, 13, and 20 were significant after Bonferroni correction, and none of these three SNPs showed significant genotype x sex interactions. Of the 30 top SNPs with significant (p < 1 x 10-4 - p < 1 x 10-6) interactions,  23 SNPs showed additive interactions and  5 SNPs showed only dominance interactions. Those SNPs showing significant associations in the regular logistic regression failed to show significant interactions. In contrast, the SNPs that showed significant interactions failed to show significant associations in models that did not incorporate interactions. It is important to consider interactions of genotype x sex in addition to associations in a GWAA of RA. Furthermore, the association between SNPs and RA susceptibility varies significantly between men and women.

Investigators in modern molecular/genetic epidemiology studies commonly analyze data on a vast number of candidate genetic markers. In such situations, rather than conventional estimation of effects (odds ratios), more accurate estimation methods are needed. The author proposes consideration of empirical Bayes and semi-Bayes methods, which yield 'adjustments for multiple estimations' by shrinking conventional effect estimates towards the overall average effect.

We describe methods based on latent class analysis for analysis and interpretation of agreement on dichotomous diagnostic ratings. This approach formulates agreement in terms of parameters directly related to diagnostic accuracy and leads to many practical applications, such as estimation of the accuracy of individual ratings and the extent to which accuracy may improve with multiple opinions. We describe refinements in the estimation of parameters for varying panel designs, and apply latent class methods successfully to examples of medical agreement data that include data previously found to be poorly fitted by two-class models. Latent class techniques provide a powerful and flexible set of tools to analyse diagnostic agreement and one should consider them routinely in the analysis of such data.

The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates in an iterative-residual fashion, and is driven by a Bonferroni-based significance score that effectively trades off between submatrix size and average value. We examine the performance and potential utility of LAS, and compare it with a number of existing methods, through an extensive three-part validation study using two gene expression datasets. The validation study examines quantitative properties of biclusters, biological and clinical assessments using auxiliary information, and classification of disease subtypes using bicluster membership. In addition, we carry out a simulation study to assess the effectiveness and noise sensitivity of the LAS search procedure. These results suggest that LAS is an effective exploratory tool for the discovery of biologically relevant structures in high dimensional data. Software is available at https://genome.unc.edu/las/.

A class of Rasch model tests is proposed, all of them based on the Mantel-Haenszel chi-squared statistic. All tests make use of the 'sufficient statistics' property the Rasch model possesses. One element of our general class, the test for item bias developed by Holland and Thayer, has been discussed extensively in the psychometric literature. Three applications of the general procedure are presented, two on unidimensionality and one on item dependence in educational testing. In each case, simulation results are reported. Our procedure is also applied to real data.

BACKGROUND: There is a need for a brief and simple screen for personality disorders that can be used in routine psychiatric assessments. AIMS: To test the concurrent validity and test-retest reliability of a brief screen for personality disorder. METHOD: Sixty psychiatric patients were administered a brief screening interview for personality disorder. On the same day, they were interviewed with an established assessment for DSM-IV personality disorder. Three weeks later, the brief screening interview was repeated in order to examine test-retest reliability. RESULTS: A score of 3 on the screening interview correctly identified the presence of DSM-IV personality disorder in 90% of participants. The sensitivity and specificity were were 0.94 and and 0.85 respectively. CONCLUSIONS: The study provides preliminary evidence of the usefulness of the screen in routine clinical settings.

The purpose of the present study was to evaluate functional connectivity of the hippocampus during a fMRI face-name learning task in a group of elders with mild memory impairment on the basis of the presence or absence of the APOE epsilon4 allele. Twelve epsilon4 carriers and 20 non-carriers with mild memory dysfunction and exhibiting equivalent performance in clinical evaluations of global cognitive function and memory were studied. Subjects underwent a fMRI session consisting of a face-name encoding memory task. Following scanning, subjects were asked to pair faces with their corresponding proper name. Functional connectivity of the hippocampus was measured by using coherence analysis to evaluate the activity of brain circuits related to memory encoding processes. In contrast to non-APOE epsilon4 allele bearers, APOE epsilon4 carriers showed enhanced connectivity with the anterior cingulate, inferior parietal/postcentral gyrus region and the caudate nucleus. Enhanced hippocampal connectivity with additional brain regions in APOE epsilon4 allele carriers during the performance of an associative memory task may reveal the existence of additional activity in the cortico-subcortical network engaged during memory encoding in subjects carrying this genetic variant.

This article advances a simple conception of test validity: A test is valid for measuring an attribute if (a) the attribute exists and (b) variations in the attribute causally produce variation in the measurement outcomes. This conception is shown to diverge from current validity theory in several respects. In particular, the emphasis in the proposed conception is on ontology, reference, and causality, whereas current validity theory focuses on epistemology, meaning, and correlation. It is argued that the proposed conception is not only simpler but also theoretically superior to the position taken in the existing literature. Further, it has clear theoretical and practical implications for validation research. Most important, validation research must not be directed at the relation between the measured attribute and other attributes but at the processes that convey the effect of the measured attribute on the test scores.

Recently, a new bedside screening test to predict the occurrence of a difficult laryngoscopy has been developed as a substitute for the Mallampati classification. The Upper-Lip-Bite test (ULBT) evaluated the patient's ability to reach or completely cover the upper lip with the lower incisors. It is often accepted that new predictive tools should undergo an external evaluation before the tool is used in clinical practice. Thus, we evaluated this test with respect to applicability, interobserver reliability, and discriminating power and compared it with the Mallampati-score (using Samsoon and Young's modification). The ULBT could not be applied in 12% of all patients (Mallampati score, <1%). However, the interobserver reliability was better for the ULBT (kappa = 0.79 versus kappa = 0.59). The discriminating power to predict a patient with difficult laryngoscopy was evaluated in 1425 consecutive patients. Both tests were assessed simultaneously in these patients by two specially trained independent observers. After the induction of anesthesia, the laryngoscopic view was assessed by the attending anesthesiologist using the classification of Cormack and Lehane. A grade I or II was called easy laryngoscopy and grade III and IV difficult laryngoscopy. The discriminating power for both tests was low (0.60 for the ULBT [95% confidence interval, 0.57-0.63] and 0.66 [0.63-0.69]) for the Mallampati score), indicating that both tests are poor predictors as single screening tests.

DIMTEST is a nonparametric hypothesis- testing procedure designed to test the assumptions of a unidimensional and locally independent item response theory model. Several previous Monte Carlo studies have found that using linear factor analysis to select the assessment subtest for DIMTEST results in a moderate to severe loss of power when the exam lacks simple structure, the ability and difficulty parameter distributions differ greatly, or the underlying model is noncompensatory. A new method of selecting the assessment subtest for DIMTEST, based on the conditional covariance dimensionality programs DETECT and HCA/ CCPROX, is presented. Simulation studies show that using DIMTEST with this new selection method has either similar or significantly higher power to detect multidimensionality than using linear factor analysis for subtest selection, while maintaining Type I error rates around the nominal level.

Genetic epidemiology is a young but rapidly developing discipline. Although its early years were largely dedicated to family-based research in monogenic disorders, now genetic-epidemiologic research increasingly focuses on complex, multifactorial disorders. Along with the development of the human-genome map and advances in molecular technology grows the importance of genetic-epidemiologic applications. Large-scale population-based studies, requiring close integration of genetic and epidemiologic research, determine future research in the field. In this paper, we review the basic principles underlying genetic-epidemiologic research, such as molecular genetics and familial aggregation of disease, as well as the typical study approaches of genome screening and candidate-gene studies.

Alternative parameterizations and problems of identification and estimation of multivariate random effects models for categorical responses are investigated. The issues are illustrated in the context of the multivariate binomial logit-normal (BLN) model introduced by Coull and Agresti (2000, Biometrics 56, 73-80). We demonstrate that the BLN model is poorly identified unless proper restrictions are imposed on the parameters. Moreover, estimation of BLN models is unduly computationally complex. In the first application considered by Coull and Agresti, an identification problem results in highly unstable, highly correlated parameter estimates and large standard errors. A probit-normal version of the specified BLN model is demonstrated to be underidentified, whereas the BLN model is empirically underidentified. Identification can be achieved by constraining one of the parameters. We show that a one-factor probit model is equivalent to the probit version of the specified BLN model and that a one-factor logit model is empirically equivalent to the BLN model. Estimation is greatly simplified by using a factor model.

When analyzing a 2 x 2 table, the two-sided Fisher's exact test and the usual exact confidence interval (CI) for the odds ratio may give conflicting inferences; for example, the test rejects but the associated CI contains an odds ratio of 1. The problem is that the usual exact CI is the inversion of the test that rejects if either of the one-sided Fisher's exact tests rejects at half the nominal significance level. Further, the confidence set that is the inversion of the usual two-sided Fisher's exact test may not be an interval, so following Blaker (2000, Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics 28, 783-798), we define the "matching" interval as the smallest interval that contains the confidence set. We explore these 2 versions of Fisher's exact test as well as an exact test suggested by Blaker (2000) and provide the R package exact2x2 which automatically assigns the appropriate matching interval to each of the 3 exact tests.

ABSTRACT: BACKGROUND: The EQ-5D is a generic questionnaire which generates a health profile as well as index scores for health-related quality of life that may be used in cost-utility analysis. Aims of the study: To examine validity and responsiveness of the EQ-5D in patients with anxiety disorders. METHODS: 389 patients with anxiety disorders completed the EQ-5D at baseline and 6-month follow-up. Subjective measures of quality of life (WHOQOL-BREF) and psychopathology (BAI, BDI-II, BSQ, ACQ, MI) were used for comparison. Validity was analyzed by assessing associations between EQ-5D scores and related other scores. Responsiveness was analyzed by calculating effect sizes of differences in scores between baseline and follow-up for 3 groups indicating more, constant or less anxiety. Meaningful difference scores for shifting to less or more anxiety were derived by means of regression analysis. RESULTS: 88.4% of respondents reported problems in at least one of the EQ-5D dimension at baseline; the mean EQ VAS score was 63.8. The EQ-5D dimension most consistently associated with the measures used for comparison was 'anxiety/depression'. EQ VAS and EQ-5D index scores were highly correlated (|r|>0.5) with scores of the WHOQOL-BREF dimensions 'physical', 'mental' and 'overall' as well as BAI and BDI-II. The EQ-5D index tended to be the most responsive score. Standardized meaningful difference scores were not significantly different between EQ VAS, EQ-5D index and measures used for comparison. CONCLUSIONS: The EQ-5D seems to be reasonably valid and moderately responsive in patients with anxiety disorders. The EQ-5D index may be suitable for calculating QALYs in economic evaluation of health care interventions for patients with anxiety disorders. Trial registration: Current Controlled Trials ISRCTN15716049.

Marginal maximum likelihood estimation is commonly used to estimate logistic-normal models. In this approach, the contribution of random effects to the likelihood is represented as an intractable integral over their distribution. Thus, numerical methods such as Gauss-Hermite quadrature (GH) are needed. However, as the dimensionality increases, the number of quadrature points becomes rapidly too high. A possible solution can be found among the Quasi-Monte Carlo (QMC) methods, because these techniques yield quite good approximations for high-dimensional integrals with a much lower number of points, chosen for their optimal location. A comparison between three integration methods for logistic-normal models: GH, QMC, and full Monte Carlo integration (MC) is presented. It turns out that, under certain conditions, the QMC and MC method perform better than the GH in terms of accuracy and computing time.

BACKGROUND: Some patients will experience more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is necessary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to track the use of HTE analyses over time, examine the appropriateness of the statistical methods used, and explore the predictors of such analyses. METHODS: Articles were selected through a probability sample of randomized controlled trials (RCTs) published in Annals of Internal Medicine, BMJ, JAMA, The Lancet, and NEJM during odd numbered months of 1994, 1999, and 2004. RCTs were independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as reporting: (1) HTE analysis, utilizing a formal test for heterogeneity or treatment-by-covariate interaction, (2) subgroup analysis only, involving no formal test for heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to identify variables associated with HTE reporting. RESULTS: 319 studies were included. Ninety-two (29%) reported HTE analysis; another 88 (28%) reported subgroup analysis only, without examining HTE formally. Major covariates examined included individual risk factors associated with prognosis, responsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Journal of publication and sample size were significant independent predictors of HTE analysis (p < 0.05 and p < 0.001, respectively). CONCLUSION: HTE is frequently ignored or incorrectly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized approach to evidence-based medicine.

This paper describes a new method of comparing the raw mark scales on two tests using expert judgment. The two tests do not need to have any common items, nor to be taken by common groups of candidates. This study used scripts (i.e. the complete work of a candidate on the test) from England's National Curriculum Test for Reading at Key Stage 3 (14-year olds) in 2003 and 2004. Each member of a panel of 12 experts was given four packs each containing ten scripts-five scripts from each year's test. Marks and annotations from these scripts had been removed. Their task was to put the ten scripts into a single rank order, based on a holistic judgment of the level of performance exhibited in each. Because the design of the study linked scripts across judges and packs it was possible to construct a single latent trait of judged quality of performance. This was done using two different analytical methods: the Rasch formulation of Thurstone paired comparisons, and the Rasch Partial Credit model. Relating the two raw mark scales to the single latent scale allowed the two years' tests to be equated. The merits of using this standard-maintaining method as opposed to a standard-setting method in this particular context are discussed.

Structured questionnaires and semi-structured interviews are often used in mixed method studies to generate confirmatory results despite differences in methods of data collection, analysis, and interpretation. A review of 19 questionnaire-interview comparison studies found that consensus and consistency statistics were generally weak between methods. Problems in aligning data from the two different methods are illustrated in a questionnaire-interview study of teacher conceptions of assessment. Poor alignment appeared attributable to: differences in data collection procedures, the complexity and instability of the construct being investigated, difficulties in making data comparable, lack of variability in participant responses, greater sensitivity to context and seemingly emotive responses within the interview, possible misinterpretation of some questionnaire prompts, and greater control of content exposure in the questionnaire. Results indicated that if `confirmatory' results are being sought, researchers must create tightly aligned and structured instruments; present the construct in a simple, concrete, and highly contextualised manner; collect the two types of data with a minimal time gap; and estimate agreement between methods using consistency statistics. However, the cost of confirmation through strong alignment may lead to the loss of rich complementary data obtained through allowing each method to be analysed in its own right.

OBJECTIVES: To assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes. DESIGN: Workplace based prospective cohort study with three 5 yearly medical screenings. PARTICIPANTS: 5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years. OUTCOME MEASURES: Non-genetic variables included in two established risk models-the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)-and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs. RESULTS: A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score. CONCLUSION: The phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.

We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%-53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20-1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2-7.0), p = 3.5 x 10(-7)] and 5kb-promoter regions [OR = 2.3 (1.5-3.6), p = 3 x 10(-4)] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34-0.58), p = 2.0 x 10(-9)]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8-2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology.

Recent important advancements in genomic research have opened the way to new strategies for public health management. One of these questions pertains to how individual genetic variation may be associated with individual variability in response to drug treatment. The field of pharmacogenetics may have a profound impact on treatment of complex psychiatric disorders like schizophrenia. However, pharmacogenetic studies in schizophrenia have produced conflicting results. The first studies examined potential associations between clinical response and drug receptor genes. Subsequent studies have tried to use more objective phenotypes still in association with drug receptor genes. More recently, other studies have sought the association between putative causative or modifier genes and intermediate phenotypes. Thus, conflicting results may be at least in part explained by variability and choice of the phenotype, by choice of candidate genes, or by the relatively little knowledge about the neurobiology of this disorder. We propose that choosing intermediate phenotypes that allow in vivo measurement of specific neuronal functions may be of great help in reducing several of the potential confounds intrinsic to clinical measurements. Functional neuroimaging is ideally suited to address several of these potential confounds, and it may represent a powerful strategy to investigate the relationship between behavior, brain function, genes, and individual variability in the response to treatment with antipsychotic drugs in schizophrenia. Preliminary evidence with potential susceptilibity genes such as COMT, DISC1, and GRM3 support these assumptions.

The present work is an introduction to Latent Class Growth Modelling (LCGM). LCGM is a semi‐parametric statistical technique used to analyze longitudinal data. It is used when the data follows a pattern of change in which both the strength and the direction of the relationship between the independent and dependent variables differ across cases. The analysis identifies distinct subgroups of individuals following a distinct pattern of change over age or time on a variable of interest. The aim of the present tutorial is to introduce readers to LCGM and provide a concrete example of how the analysis can be performed using a real‐world data set and the SAS software package with accompanying PROC TRAJ application. The advantages and limitations of this technique are also discussed.

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

Twin studies indicate that both intelligence and brain structure are moderately to highly heritable. Recent bivariate studies of adult twins also suggest that intelligence and brain morphometry are influenced by shared genetic factors. The current study examines shared genetic and environmental factors between brain morphometry and intelligence in a sample of children and adolescents (twins, twin siblings, and singletons; n = 649, ages 4-19). To extend previous studies, brain morphometric data were parsed into subregions (lobar gray/white matter volumes, caudate nucleus, lateral ventricles) and intelligence into verbal and nonverbal skills (Wechsler Vocabulary and Block Design subtests). Phenotypic relationships between brain volumes and intelligence were small. Verbal skills shared unique environmental effects with gray matter volumes while nonverbal skills shared genetic effects with both global and regional gray and white matter. These results suggest that distinct mechanisms contribute to the small phenotypic relationships between brain volumes and verbal versus nonverbal intelligence.

Maximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene-environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of scripts describing relatively simple models to be estimated by the freely obtainable BUGS software. In addition, inference using BUGS is illustrated using a data set on follicle-stimulating hormone and luteinizing hormone levels with repeated measures. The examples provided can serve as stepping stones for more complicated models, tailored to the specific needs of the individual researcher.

Novelty Seeking (NS) is a human personality trait in which impulsive, exploratory, and thrill-seeking behaviors are displayed. Dopaminergic genes have been prime candidates in the search for the genetic factors underlying NS because of the central role that dopamine plays in the brain's reward system. We have investigated whether there is an association between a polymorphic 120 base pairs (bp) repeat that is located in the 5'-untranslated region of the dopamine D4 receptor gene (DRD4) and NS. We genotyped four separate groups from psychiatric clinical studies for the repeat polymorphism. There were significant associations with NS in the groups of bipolar (P = 0.01) and alcoholic (P = 0.006) families containing 267 and 172 subjects, respectively. Subjects who were homozygous for the single-copy allele (SS genotype) had higher mean NS scores. This trend was also observed in the two other studies that contained unrelated subjects diagnosed with depression (N = 143 and N = 148) but the associations between DRD4 duplication genotype and NS were not significant in these groups. In the data combined from all four clinical groups those genotyped as SS had higher mean scores for all four NS subscales with significant associations for impulsivity (P = 0.0006), extravagance (P = 0.04), disorderliness (P = 0.02), and total NS (P = 0.0003). However, given the low frequency of the single-copy allele, this polymorphism would account for only a small proportion of the variance of NS in the population.

The purposeof this project was to evaluate statisticalproceduresfor assessingdifferential item functioning(DIF) in polytomousitems (items with more than two scorecategories). Three descriptivestatistics-theStandardized Mean Difference, or SMD (Dorans & Schmitt, 1991), and two proceduresbased on SIBTEST (Shealy & Stout, 1993) were considered,along with five inferential procedures-twobasedon SMD, two basedon SIBTEST, and the Mantel (1963) method. The DIF procedureswere evaluatedthroughapplicationsto simulated data, as well as data from ETS tests. The simulation includedconditionsin which the two groupsof examinees had the same ability distribution and conditionsin which the group means differed by one standarddeviation. When the two groupshad the same distribution, the descriptive index that performed best was the SMD. When the two groupshad different distributions,a modified form of the SIBTEST DIF effect size measuretended to perform best. The five inferential procedures performed almost indistinguishablywhen the two groupshad identical distributions. When the two groupshad different distributionsand the studied item washighly discriminating,theSIBTEST proceduresshowedmuchbetter Type I error control than did the SMD and Mantel methods,particularly in short tests. The power ranking of the five procedureswas inconsistent;it dependedon thedirectionofDIF andotherfactors. Routine applicationof thesepolytomousDIF methodsat ETS seems feasible in caseswhere a reliable test is available for matching examinees. For the Mantel and SMD methods,Type I error control may be a concernunder certainconditions. In thecaseof SIBTEST, thecurrentversioncannoteasily accommodatematchingteststhatdonotusenumber-rightscoring. Additional researchin theseareasis likely to be useful.

Psychiatric case-identification in general populations allows us to study both individuals with functional psychiatric disorders and the populations from which they come. The individual level of analysis permits disorders to be related to factors of potential aetiological significance and the study of attributes of the disorders that need to be assessed in non-referred populations (an initially scientific endeavour). At the population level valid case identification can be used to evaluate needs for treatment and the utilization of service resources (a public health project). Thus, prevalence is of interest both to scientists and to those responsible for commissioning and planning services (Brugha et al. 1997; Regier et al. 1998). The quality of case identification techniques and of estimates of prevalence is thus of general concern (Bartlett & Coles, 1998). Structured diagnostic interviews were introduced into general population surveys in the 1970s as a method `to enable interviewers to obtain psychiatric diagnoses comparable to those a psychiatrist would obtain' (Robins et al. 1981). The need to develop reliable standardized measures was partly driven by an earlier generation of prevalence surveys showing rates ranging widely from 10·9% (Pasamanick et al. 1956) to 55% (Leighton et al. 1963) in urban and rural North American communities respectively. If the success of large scale psychiatric epidemiological enquiries using structured diagnostic interviews and standardized classifications is measured in terms of citation rates it would seem difficult to question. But the development of standardized interviews of functional psychiatric disorders has not solved this problem of variability: the current generation of large scale surveys, using structured diagnostic interviews and serving strictly defined classification rules, have generated, for example, 12-month prevalence rates of major depression in the US of 4·2% (Robins & Regier, 1991) and 10·1% (Kessler et al. 1994). This calls into question the validity of the assessments, such that we must reopen the question of what they should be measuring and how they should do it.

In this paper, we propose a two-step method to identify and estimate endogenous and exogenous social interactions in Manski (1993) and Brock and Durlauf's (2001a,b) discrete choice model with unobserved group variables. Taking advantage of social groups with large group sizes, we first estimate a probit model with group fixed-effects, and then use the instrumental variables method to estimate endogenous and exogenous group effects via the group fixed-effect estimates. Our method does not depend on distributional assumptions on unobserved group variables, and are computationally simple. When there are multiple equilibria, we take advantage of large group sizes to estimate endogenous and exogenous group effects without the need to specify an (arbitrary) equilibrium selection mechanism. The paper provides an extensive Monte Carlo study on the finite sample performance of such estimators.

The methods to detect gene-gene interactions between variants in genome-wide association study (GWAS) datasets have not been well developed thus far. PLATO, the Platform for the Analysis, Translation and Organization of large-scale data, is a filter-based method bringing together many analytical methods simultaneously in an effort to solve this problem. PLATO filters a large, genomic dataset down to a subset of genetic variants, which may be useful for interaction analysis. As a precursor to the use of PLATO for the detection of gene-gene interactions, the implementation of a variety of single locus filters was completed and evaluated as a proof of concept. To streamline PLATO for efficient epistasis analysis, we determined which of 24 analytical filters produced redundant results. Using a kappa score to identify agreement between filters, we grouped the analytical filters into 4 filter classes; thus all further analyses employed four filters. We then tested the MAX statistic put forth by Sladek et al. 1 in simulated data exploring a number of genetic models of modest effect size. To find the MAX statistic, the four filters were run on each SNP in each dataset and the smallest p-value among the four results was taken as the final result. Permutation testing was performed to empirically determine the p-value. The power of the MAX statistic to detect each of the simulated effects was determined in addition to the Type 1 error and false positive rates. The results of this simulation study demonstrates that PLATO using the four filters incorporating the MAX statistic has higher power on average to find multiple types of effects and a lower false positive rate than any of the individual filters alone. In the future we will extend PLATO with the MAX statistic to interaction analyses for large-scale genomic datasets.

Previous attempts at incorporating expert test construction practices into computerized adaptive testing paradigms are described. A new method is presented for incorporating a large number of con straints on adaptive item selection. The meth odology emulates the test construction practices of expert test specialists, which is a necessity if com puterized adaptive testing is to compete with con ventional tests. Two examples-one for a verbal measure and the other for a quantitative measure- are provided of the successful use of the proposed method in designing adaptive tests.

BACKGROUND: The overlap between Depression and Anxiety has led some researchers to conclude that they are manifestations of a broad, non-specific neurotic disorder. However, others believe that they can be distinguished despite sharing symptoms of general distress. The Tripartite Model of Affect proposes an anxiety-specific, a depression-specific and a shared symptoms factor. Watson and Clark developed the Mood and Anxiety Symptom Questionnaire (MASQ) to specifically measure these Tripartite constructs. Early research showed that the MASQ distinguished between dimensions of Depression and Anxiety in non-clinical samples. However, two recent studies have cautioned that the MASQ may show limited validity in clinical populations. The present study investigated the clinical utility of the MASQ in a clinical sample of adolescents and young adults. METHODS: A total of 204 Young people consecutively referred to a specialist public mental health service in Melbourne, Australia were approached and 150 consented to participate. From this, 136 participants completed both a diagnostic interview and the MASQ. RESULTS: The majority of the sample rated for an Axis-I disorder, with Mood and Anxiety disorders most prevalent. The disorder-specific scales of the MASQ significantly discriminated Anxiety (61.0%) and Mood Disorders (72.8%), however, the predictive accuracy for presence of Anxiety Disorders was very low (29.8%). From ROC analyses, a proposed cut-off of 76 was proposed for the depression scale to indicate 'caseness' for Mood Disorders. The resulting sensitivity/specificity was superior to that of the CES-D. CONCLUSION: It was concluded that the depression-specific scale of the MASQ showed good clinical utility, but that the anxiety-specific scale showed poor discriminant validity.

BACKGROUND: The high heritability of the core symptoms of attention-deficit/hyperactivity disorder (ADHD) has been repeatedly demonstrated, but few studies to date have investigated the extent to which the same genetic influences operate across development or new genes emerge at different developmental periods. METHODS: We report data from a large, population-based study of approximately 4,000 twin pairs, who have been followed up from early to middle childhood. RESULTS: Parents' ratings of ADHD symptoms showed moderate stability across the ages, which was mainly due to shared genetic influences. There was also evidence of additional genetic influences, which were not shared with those acting earlier on, emerging at later age periods. The contribution of environmental influences to the stability of the ADHD symptoms over time was small. Parents' ratings on the Conners' DSM-IV ADHD subscale at the last assessment point, at an average age of 8 years, did not show the rater contrast effects that were observed in the parents' ratings at earlier ages with briefer measures. Similar estimates of genetic and environmental influences were obtained for girls and boys. CONCLUSIONS: We discuss the implications of the findings for molecular genetic studies on ADHD symptomatology.

BACKGROUND: Antisocial behavior and substance dependence disorders exact a heavy financial and human cost on society. A better understanding of the mechanisms of familial transmission for these "externalizing" disorders is necessary to better understand their etiology and to help develop intervention strategies. OBJECTIVES: To determine the extent to which the family transmission of externalizing disorders is due to a general vs a disorder-specific vulnerability and, owing to the genetically informative nature of our data, to estimate the heritable vs environmental nature of these transmission effects. DESIGN: We used structural equation modeling to simultaneously estimate the general and specific transmission effects of 4 externalizing disorders: conduct disorder, adult antisocial behavior, alcohol dependence, and drug dependence. SETTING: Participants were recruited from the community and were interviewed in a university laboratory. PARTICIPANTS: The sample consisted of 542 families participating in the Minnesota Twin Family Study. All families included 17-year-old twins and their biological mother and father. MAIN OUTCOME MEASURES: Symptom counts of conduct disorder, the adult criteria for antisocial personality disorder, alcohol dependence, and drug dependence. RESULTS: Transmission of a general vulnerability to all the externalizing disorders accounted for most familial resemblance. This general vulnerability was highly heritable (h2 = 0.80). Disorder-specific vulnerabilities were also detected for conduct disorder, alcohol dependence, and drug dependence. CONCLUSIONS: The mechanism underlying the familial transmission of externalizing disorders is primarily a highly heritable general vulnerability. This general vulnerability or common risk factor should be the focus of research regarding the etiology and treatment of externalizing disorders.

In 1988, Becker and Murphy [Becker, G.S., Murphy, K.M., 1988. A theory of rational addiction. Journal of Political Economy, 96, 675-700.] launched a theory in which they proposed that the perspective of rational decision-making could be applied also to cases of addictive behaviour. This paper discusses the theory's assumptions of interpersonal variation and stability in time preferences on the basis of estimates derived from three groups of people with different consumption levels of illegal intoxicants. We find that active injectors of heroin and amphetamine have a higher discount rate than a group reporting that they have never used the substances. Of greater interest, though not in accordance with Becker and Murphy's assumption of stability, we also find that the discount rate among active and former users differs significantly. These findings raise the question of whether a high time-preference rate leads to addiction or whether the onset of an addiction itself alters people's inter-temporal equilibrium.

Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg's methods, are now applicable to dependent data. Continuous random field methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We find that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom. We also show the limitations of trying to find an equivalent number of independent tests for an image of correlated test statistics.

Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplic- ity and its (apparent) universality. Many results exist on model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.

The standard analysis of variance (ANOVA) method is usually applied to analyse continuous data from cross-over studies. The method, however, has been known to be not robust for general variance-covariance structure. The simple empirical generalized least squares (EGLS) method, proposed in an attempt to improve the precision of the standard ANOVA method for general variance-covariance structure, is usually insufficient for small-sample cross-over trials. In this paper we compare the following commonly used or recent approaches: standard ANOVA; simple EGLS; modified ANOVA method derived from a modified approximate F-distribution; and a modified EGLS method adjusted by the Kenward and Roger procedure in terms of robustness and power while applying to small-sample cross-over studies (say, the sample size is less than 40) over a variety of variance-covariance structures by simulation. We find that the unconditional modified ANOVA method has robust performance for all of the simulated small-sample cross-over studies over the various variance-covariance structures, and has comparable power with the standard ANOVA method whenever they are comparable in type I error rate. The EGLS method (simple or modified) is not reliable when the sample size of a cross-over study is too small, say, less than 24 in the simulation, unless a simple covariance structure is correctly assumed. Given a relatively larger sample size, the modified EGLS method, assuming an unstructured covariance matrix, demonstrates robust performance over the various variance-covariance structures in the simulation and provides more powerful tests than those of the modified (or standard) ANOVA method.

Visual perceptual skills of school-age children are often assessed using the Supplemental Developmental Test of Visual Perception of the Developmental Test of Visual-Motor Integration. The study purpose was to consider the construct validity of this test by evaluating its scalability (interval level measurement), unidimensionality, differential item functioning, and hierarchical ordering of its items. Visual perceptual performance scores from a sample of 356 typically developing children (171 boys and 185 girls ages 5 to 11 years) were used to complete a Rasch analysis of the test. Seven items were discarded for poor fit, while none of the items exhibited differential item functioning by sex. The construct validity, scalability, hierarchical ordering, and lack of differential item functioning requirements were met by the final test version. Since 7 test items did not fit the Rasch analysis specifications, the clinical value of the test is questionable and limited.

BACKGROUND: Although insulin therapy is well-accepted by symptomatic diabetic patients, it is still often delayed in less severe patients, in whom injectable insulin remains under-used. A better understanding of patients' perception of insulin would eventually help physicians to adopt the most appropriate dialogue when having to motivate patients to initiate or to intensify insulin injection. METHODS: The 'Studying the Hurdles of Insulin Prescription' (SHIP) questionnaire was developed based on a list of concepts derived from three diabetic patients' focus groups, and was included into two cross-sectional studies with similar design: SHIP Oral study and SHIP Premix study. Diabetic patients treated with oral hypoglycaemic agents (OHA; n = 1,494) and patients already treated with insulin (n = 1,150) completed the questionnaire at baseline, 6- and 12 months. Psychometric properties were assessed: 1) structure analysis by Principal Component Analysis (PCA) with Varimax rotation, 2) internal consistency reliability (Cronbach's alpha), and 3) concurrent validity (Spearman correlation coefficients with the Fear of Self-Injecting (FSI) score of the Diabetes Fear of Injecting and Self-testing Questionnaire. Reluctance/motivation towards insulin was assessed. Scores' ability to predict patients' insulin injection reluctance/motivation and initiation/intensification was evaluated with the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). RESULTS: PCA analysis confirmed the structure of the 14 items grouped into 3 dimensions: 'acceptance and motivation', 'fear and constraints', and 'restraints and barriers' towards insulin injection. Internal consistency reliability was excellent (Cronbach's alpha > 0.70); concurrent validity was good. The three scores were significantly predictive of patients' reluctance/motivation towards insulin injection initiation, as they were of patients' actual switch, except for the 'restraints and barriers' dimension. 'Acceptance and motivation' and 'fears and constraints' dimensions were also significantly predictive of patients' reluctance/motivation towards insulin intensification. By the end of the 12-month study, 179 of the initially OHA-treated patients had started insulin injections; 186 of the patients already treated with insulin had increased their injections. CONCLUSION: The SHIP questionnaire provides reliable and valid assessment of diabetic patients' attitude towards insulin and injections. The predictive power of scores for patients' reluctance/motivation and actual treatment decisions demonstrates encouraging potential for further application in clinical practice.

Early detection of developmental problems improves outcomes for parents and children. Parents want to be involved in assessment and need high-quality, accurate, and reliable data on child development to help monitor progress and inform decisions on referral. The aim of this paper is to review which websites are readily accessible to parents on child development and to assess their quality. An internet search (on Google and Yahoo) was conducted using the search terms 'child development', 'parenting', and 'developmental milestones'. Criteria were agreed for evaluating web-based resources, adapted from and based on previously reported methods. Data were collected on site content, diagrams and layout, readability (Flesch Reading Ease Scale), design, navigability, overall design, and interactive features. Forty-four relevant websites were identified for further analysis: six government, three university, 15 health-care professional, four American Academy of Pediatrics, 10 by journalists, and six undisclosed. The best websites are presented, with justification for their choice. Overall, information available for parents about child development is accurate but much of it is incomplete, unclear, or difficult to access. There is a need to develop an easily accessible, clear, and authoritative resource for parents with illustrations. Focus groups are being held to inform this research further.

Ideal point estimation, a variation of item response theory models, has been widely used by political scientists to analyze legislative behaviors. However, many existing ideal point estimation research is based on unrealistic assumptions of independence of different individuals' decisions towards all cases/bills and the independence of one's decisions towards different cases/bills. The violation of such assumptions leads to bias and inefficiency in parameter estimation. More im- portantly, failing to address these assumptions has hampered the ideal point estimation research from offering intuitive and concise explanations on complex legislative behaviors such as multidi- mensionality, strategic voting, temporary coalitions. In this paper, we extend one testlet response theory model by Bradlow, Wainer and Wang(1999) to a comprehensive hierarchical Bayesian statistical framework that allows researchers to model inter-individual and intra-individual cor- relations through random effects and/or fixed effects. Through simulations and an analysis of the US Supreme Court vote cast data, we show that the proposed framework holds good promise for tackling many unsettled issues in ideal point estimations. As a companion to this paper, we also offer an easy-to-use R package with C code that implements the methods discussed herein.

Generalized anxiety disorder (GAD) is a chronic illness that leads to substantial impairments in quality of life. This post-hoc analysis used combined data from three 8-week quetiapine extended-release trials to investigate the reliability, validity, and responsiveness of the Short Form of the Quality of Life Enjoyment and Satisfaction Questionnaire [Q-LES-Q (SF)] in 2588 patients with GAD. The baseline Q-LES-Q (SF) score showed a Cronbach's alpha value of 0.86, indicative of reliability. Validity analyses for Q-LES-Q (SF) identified significant correlations with clinical efficacy measures (r>0.34 at week 8; P<0.001) and significant discrimination between patient groups categorized by symptom severity (P<0.001). Responsiveness was shown by significant differences in mean changes in Q-LES-Q (SF) scores at week 8 between patients defined according to the Hamilton Rating Scale for Anxiety response or remission criteria (P<0.001). The minimum clinically important Q-LES-Q (SF) score change was identified to be 6.80 points. Using this definition, response rates were significantly greater with quetiapine extended-release 150 mg versus placebo in individual trials and the combined population (P < or = 0.02). This analysis shows the overall reliability, validity, and responsiveness of the Q-LES-Q (SF) as a measure of overall quality of life and satisfaction in patients with GAD.

OBJECTIVE: To compare changes in quality of life (QoL) over 96 weeks in patients enrolled in a triple-therapy protocol, a treatment-intensification protocol, or an induction-maintenance therapy protocol, and to compare QoL between patients who continued and discontinued their antiretroviral regimen. PATIENTS: Naive patients enrolled in a triple-therapy protocol (zidovudine/lamivudine or stavudine/didanosine or stavudine/lamivudine supplemented with protease inhibitor therapy of choice) (n = 35), a protocol of treatment intensification (ritonavir/saquinavir or ritonavir/saquinavir/stavudine) (n = 74) in which therapy was intensified with nucleoside analogue(s) in cases of insufficient viral suppression, and a protocol of induction (saquinavir/nelfinavir/lamivudine/ stavudine) maintenance (saquinavir/nelfinavir or stavudine/nelfinavir) therapy (n = 50). MAIN OUTCOME MEASURE: Changes from baseline in QoL assessed by the Medical Outcomes Study HIV Health Survey at weeks 0, 12, 24, 36, 48, 72 and 96. RESULTS: Patients in the triple-therapy and treatment-intensification protocols showed more favourable changes in physical function, social function, mental health, energy/fatigue, health distress and overall QoL compared to patients in the induction-maintenance protocol, with patients in the first two protocols showing improvements in QoL and those in the induction-maintenance protocol showing declining or unchanged QoL. Patients who discontinued study medication due to insufficient efficacy, toxicities or at their own request showed less favourable changes in QoL compared with patients who continued their regimen. The highest proportion of discontinuations was within the induction-maintenance protocol. CONCLUSION: Antiretroviral treatment strategies that are effective and tolerable have the potential to improve patients' QoL over 96 weeks.

Aggressive behavior is influenced by variation in genes of the serotonergic circuitry and early-life experience alike. The present study aimed at investigating the contribution of polymorphisms shown to moderate transcription of two genes involved in serotonergic neurotransmission (serotonin transporter, 5HTT, and monoamine oxidase A, MAOA) to the development of violence and to test for gene-environment interactions relating to adverse childhood environment. A cohort of 184 adult male volunteers referred for forensic assessment participated in the study. Each individual was assigned to either a violent or a nonviolent group. Logistic regression was performed and the best-fitting model, with a predictive power of 74%, revealed independent effects of adverse childhood environment and MAOA genotype. High environmental adversity during childhood was associated significantly with violent behavior. Forty-five percent of violent, but only 30% of nonviolent individuals carried the low-activity, short MAOA allele. Most interestingly, an interaction effect between childhood environment and 5HTT genotype on violent behavior was found in that high adversity during childhood impacted only the later-life violence if the short promoter alleles were present. These findings indicate complex interactions between genetic variation of the serotonergic circuitry and environmental factors arguing against simplistic, mono-causal explanations of violent behavior.

Independent clusters, as treated in classical linear factor analysis, provide a desirable basis for multidimensional item response models, yielding interpretable and useful results. The independent- clusters basis serves to determine dimensionality, while establishing a pattern for the item parameter matrix that provides identifiability conditions and facilitates interpretation of the traits. It also provides a natural extension of known results on convergent/discriminant “construct” validity to binary items, allowing the quantification of the validity of test and subtest scores. The independent-clusters basis simplifies item/test response and information hypersurfaces, which cannot otherwise be easily studied except in the trivial case of two dimensions, and provides estimates of latent traits with uncorrelated measurement errors. In addition, the affine transformation needed for the informative analysis of the causes of differential item functioning is simplified using the independent-clusters basis. These classically based procedures are already well-established in the context of the linear common- factor model and, accordingly, they set a standard against which more recently developed procedures for the same purposes need to be judged.

The unified biosocial theory of personality, proposed by Cloninger, conceptualises personality as a combination of heritable, neurobiologically based traits (temperament dimensions), and traits reflecting sociocultural learning (character dimensions). The temperament dimensions are thought to be related to activity in specific central neurotransmitter systems. The relationship of the dimensions of the Temperament and Character Inventory, particularly harm avoidance (HA), and platelet 5-HT2 receptor sensitivity was investigated in a sample of undergraduate student volunteers (N = 49). Serotonin-receptor binding results in Ca2+ release from intracellular stores. The concentration of serotonin required to produce half maximal Ca2+ response (EC50) is indicative of 5-HT2 receptor sensitivity such that the lower the EC50 serotonin concentration, the greater the 5-HT2 receptor sensitivity. A significant inverse correlation was found between HA and EC50 (r = -0.644, P < 0.001). Self-directedness was also significantly correlated with EC50 (r = 0.391, P = 0.005). Novelty seeking, a personality trait similar to sensation seeking, was not significantly correlated with serotonin.

BACKGROUND: Fatigue is a common and debilitating symptom in multiple sclerosis (MS). Best-practice guidelines suggest that health services should repeatedly assess fatigue in persons with MS. Several fatigue scales are available but concern has been expressed about their validity. The objective of this study was to examine the reliability and validity of a new scale for MS fatigue, the Neurological Fatigue Index (NFI-MS). METHODS: Qualitative analysis of 40 MS patient interviews had previously contributed to a coherent definition of fatigue, and a potential 52 item set representing the salient themes. A draft questionnaire was mailed out to 1223 people with MS, and the resulting data subjected to both factor and Rasch analysis. RESULTS: Data from 635 (51.9% response) respondents were split randomly into an 'evaluation' and 'validation' sample. Exploratory factor analysis identified four potential subscales: 'physical', 'cognitive', 'relief by diurnal sleep or rest' and 'abnormal nocturnal sleep and sleepiness'. Rasch analysis led to further item reduction and the generation of a Summary scale comprising items from the Physical and Cognitive subscales. The scales were shown to fit Rasch model expectations, across both the evaluation and validation samples. CONCLUSION: A simple 10-item Summary scale, together with scales measuring the physical and cognitive components of fatigue, were validated for MS fatigue.

ABSTRACT: BACKGROUND: The 12-item General Health Questionnaire (GHQ-12) is used routinely as a unidimensional measure of psychological morbidity. Many factor-analytic studies have reported that the GHQ-12 has two or three dimensions, threatening its validity. It is possible that these 'dimensions' are the result of the wording of the GHQ-12, namely its division into positively phrased (PP) and negatively phrased (NP) statements about mood states. Such 'method effects' introduce response bias which should be taken into account when deriving and interpreting factors. METHODS: GHQ-12 data were obtained from the 2004 cohort of the Health Survey for England (N = 3705). Following exploratory factor analysis (EFA), the goodness of fit indices of one, two and three factor models were compared with those of a unidimensional model specifying response bias on the NP items, using structural equation modelling (SEM). The hypotheses were (1) the variance of the responses would be significantly higher for NP items than for PP items because of response bias, and (2) that the modelling of response bias would provide the best fit for the data. RESULTS: Consistent with previous reports, EFA suggested a two-factor solution dividing the items into NP and PP items. The variance of responses to the NP items was substantially and significantly higher than for the PP items. The model incorporating response bias was the best fit for the data on all indices (RMSEA = 0.068, 90%CL = 0.064, 0.073). Analysis of the frequency of responses suggests that the response bias derives from the ambiguity of the response options for the absence of negative mood states. CONCLUSION: The data are consistent with the GHQ-12 being a unidimensional scale with a substantial degree of response bias for the negatively phrased items. Studies that report the GHQ-12 as multidimensional without taking this response bias into account risk interpreting the artefactual factor structure as denoting 'real' constructs, committing the methodological error of reification. Although the GHQ-12 seems unidimensional as intended, the presence of such a large response bias should be taken into account in the analysis of GHQ-12 data.

OBJECTIVE: To compare the measurement properties of the Modified Health Assessment Questionnaire [MHAQ], the SF-36((R)) Health Survey 10 item Physical Functioning scale [PF10], and scores from an item response theory (IRT) based scale combining the two measures. STUDY DESIGN: Rheumatoid arthritis (RA) patients (n = 339) enrolled in a multi-center, randomized, double-blind, placebo-controlled trial completed the MHAQ and the SF-36 pre- and post-treatment. Psychometric analyses used confirmatory factor analysis and IRT models. Analyses of variance were used to assess sensitivity to changes in disease severity (defined by the American College of Rheumatism (ACR)) using change scores in MHAQ, PF10, and IRT scales. Analyses of covariance were used to assess treatment responsiveness. RESULTS: For the entire score range, the 95% confidence interval around individual patient scores was smaller for the combined (total) IRT based scale than for other measures. The MHAQ and PF10 were about 70% and 50% as efficient as the total IRT score of physical functioning in discriminating among ACR groups, respectively. The MHAQ and PF10 were also less efficient than the total IRT score in discriminating among treatment groups. CONCLUSIONS: Combining scales from the two short forms yields a more powerful tool with greater sensitivity to treatment response.

A method is presented to draw rectangles to represent categorical data relationships. The idea is an adaptation of a scaled Venn diagram. Rectangles are drawn with area proportional to the frequency of categories and the rectangles are positioned to overlap each other so that the areas of overlap are in proportion to the joint frequencies of the characteristics. The diagrams are especially useful to illustrate symptom co-occurrence.

Recently, Lee and Ashton (2004) described the HEXACO Personality Inventory (HEXACO-PI), a new instrument designed to assess the six dimensions observed in lexical studies of personality structure of var- ious languages. Here, we describe the development of an alternative measure of the HEXACO factors and their facets, using the items of the International Personality Item Pool (IPIP). The scales of the resulting IPIP-HEXACO inventory showed satisfactory psychometric properties, as assessed by internal-consistency reliability, convergent and discriminant correlations with the original HEXACO-PI scales, and factor struc- ture. We discuss the potential usefulness of the new IPIP-HEXACO inventory and its strengths and limitations.

BACKGROUND & AIMS: The Cdcs1 locus of the C3Bir mouse confers severe colitis associated with a decrease in innate immune function and an increase in adaptive T-cell responses to commensal bacterial products. The aim of our study was to determine if defects in innate immunity are similarly associated with increased adaptive immune responses to microbial antigens in Crohn's disease patients. METHODS: Sera from 732 patients, 220 unaffected relatives, and 200 healthy controls were tested for antibodies to oligomannan, the Pseudomonas fluorescens-related protein, Escherichia coli outer membrane porin C, CBir1 flagellin, and DNA from the same subjects was tested for 3 Crohn's disease-associated variants of the NOD2 gene, and 5 toll-like receptor (TLR) 2, 2 TLR4, and 2 TLR9 variants. The magnitude of responses to microbial antigens was examined according to variant status. RESULTS: NOD2 variant carriage increased in frequency with increasing number of positive antibodies and increasing cumulative quantitative response as measured by quartile sum (P for trend, .0008 and .0003, respectively). Mean antibody and quartile sums were higher for patients carrying any NOD2 variant versus those carrying none (2.24 vs 1.92 and 10.60 vs 9.72; P = .0008 and P = 0.0003, respectively). The mean quartile sum was higher for unaffected relatives carrying any NOD2 variant versus those carrying none (10.67 vs 9.75, respectively; P = .02). No association was found between any TLR variant and the magnitude of response. CONCLUSIONS: Patients with Crohn's disease and unaffected relatives carrying variants of the NOD2 gene have increased adaptive immune responses to microbial antigens.

The three-arm design with test treatment, reference treatment and placebo offers internal assay sensitivity for a proof of non-inferiority. In this design the relative effects known from nonparametric theory are robust tools allowing the assessment of non-inferiority in a range of situations. An asymptotic nonparametric theory is established in the three-arm design based on the asymptotic distribution of rank means under alternative. A rank test for non-inferiority is derived. Fieller's formula is used to calculate a respective confidence interval. The approach is expanded to multicentre studies. The simulation studies are conducted demonstrating the accuracy of the methods and an example is discussed.

BicOverlapper is a tool to visualize biclusters from gene-expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. AVAILABILITY: The BicOverlapper software and supplementary material are available at http://vis.usal.es/bicoverlapper

Several sources of errors are discussed. While genotyping errors have little effect on power in case-control association studies, they tend to strongly increase false positive results in TDT type tests unless occurrence of errors is allowed for in the analysis (e.g., TDTae test). Disregarding non-genetic risk factors is shown to lead to a form of hidden heterogeneity, which can strongly reduce power. Stratification of data into more homogeneous subgroups is advocated as a simple solution to allowing for non-genetic risk factors such as socio-economic status and food preferences.

BACKGROUND: The Perceived Impact of Problem Profile (PIPP) was developed to provide a tool for measuring the impact of a health condition from the individual's perspective, using the ICF model as a framework. One of the aims of the ICF is to enable the comparison of data across countries, however, relatively little is known about the subjective experience of disability in middle and low-income countries. The aim of this study was to assess the validity of the Perceived Impact of Problem Profile (PIPP) for use among adults with a disability in Thailand using Rasch analysis. METHODS: A total of 210 adults with mobility impairment from the urban, rural and remote areas of northeast Thailand completed the PIPP, which contains 23 items assessing both impact and distress across five key domains (Self-care, Mobility, Participation, Relationships, and Psychological Well-being). Rasch analysis, using RUMM2020, was conducted to assess the internal validity and psychometric properties of the PIPP Impact subscales. Validation of the PIPP Impact scales was conducted by comparing scores across the different response levels of the EQ5D items. RESULTS: Rasch analysis indicated that participants did not clearly differentiate between 'impact' and 'distress,' the two aspects assessed by the PIPP. Further analyses were therefore limited to the PIPP Impact subscales. These showed adequate psychometric properties, demonstrating fit to the Rasch model and good person separation reliability. Preliminary validity testing using the EQ5D items provided support for the PIPP Impact subscales. CONCLUSION: The results provide further support for the psychometric properties of the PIPP Impact scales and indicate that it is a suitable tool for use among adults with a locomotor disability in Thailand. Further research is needed to validate the PIPP across different cultural contexts and health conditions and to assess the usefulness of separate Impact and Distress subscales.

Associations between reaction times and mental ability test scores have been widely reported in the literature on the information processing theories of psychometric intelligence. There have been varying estimates of the strength of these associations, which are typically reported in terms of correlation coefficients. In a previous article, we reported correlations between scores on Part 1 of the Alice Heim 4 and simple and four-choice reaction time of -.31 and -.49, respectively, derived from a population based sample of 900 residents of the West of Scotland aged 56. The use of the Pearson, or product moment, correlation coefficient to summarise the association between reaction time and mental test ability assumes that they jointly have a bivariate normal distribution and that the relationship between them is linear. The differentiation hypothesis can be construed as implying that the relationship should be nonlinear with a stronger relationship at lower levels of mental ability. We examined in detail the relationships underlying these correlations to assess whether they adequately represented the strength of the association and to test for any departure from linearity. For four-choice reaction time, the correlation is a good summary of the relation to AH4 score. However, the relation of AH4 and simple reaction time is more complex and nonlinear.

OBJECTIVE: To develop and evaluate a prototype measure (OA-DISABILITY-CAT) for osteoarthritis research using item response theory (IRT) and computer-adaptive test (CAT) methodologies. STUDY DESIGN AND SETTING: We constructed an item bank consisting of 33 activities commonly affected by lower extremity (LE) osteoarthritis. A sample of 323 adults with LE osteoarthritis reported their degree of limitation in performing everyday activities, and completed the Health Assessment Questionnaire-II (HAQ-II). We used confirmatory factor analyses to assess scale unidimensionality and IRT methods to calibrate the items and examine the fit of the data. Using CAT simulation analyses, we examined the performance of OA-DISABILITY-CATs of different lengths compared with the full-item bank and the HAQ-II. RESULTS: One distinct disability domain was identified. The 10-item OA-DISABILITY-CAT demonstrated a high degree of accuracy compared with the full-item bank (r=0.99). The item bank and the HAQ-II scales covered a similar estimated scoring range. In terms of reliability, 95% of OA-DISABILITY reliability estimates were over 0.83 vs. 0.60 for the HAQ-II. Except at the highest scores, the 10-item OA-DISABILITY-CAT demonstrated superior precision to the HAQ-II. CONCLUSION: The prototype OA-DISABILITY-CAT demonstrated promising measurement properties compared with the HAQ-II, and is recommended for use in LE osteoarthritis research.

The measurement of health-related quality of life (HRQOL) in patients infected with the HIV virus is becoming increasingly important in clinical trials of antiretroviral drugs, principally for two reasons: (1) the disease is becoming a chronic illness, in which the measurement of `quality of survival' is gaining relevance as an outcome measure; and (2) the antiretroviral drugs used to treat the disease may have severe side effects which, in turn, affect the HRQOL of patients and threaten compliance with treatment. A bibliographic review was conducted to locate currently existing HRQOL instruments developed specifically for HIV patients, with demonstrated sensitivity to change, and which may be used as a measure of clinical outcomes. The review also aimed to determine the frequency and adequacy of incorporation of HRQOL as an outcome variable in clinical trials and other prospective drug trials completed between 1990 and 1998 in this group of patients. Although 7 HIV-specific HRQOL instruments with demonstrated sensitivity to change were located (MOS-HIV, MQOL-HIV, HIV-PARSE, HOPES, EORTC-QLQ-HIV, FAHI and AIDS-HAQ), only 2.35% of publications were found to incorporate HRQOL as an outcomes variable. In addition, fewer than 40% of the instruments used to measure HRQOL in the publications reviewed were specific to HIV patients and had demonstrated sensitivity to change. This may be due to: (1) the lack, until relatively recently, of adequate instruments for measuring of HRQOL and changes in HRQOL in HIV patients; (2) the difficulty of measuring HRQOL in HIV patients and; (3) the low importance given to this aspect of the disease to date.

BACKGROUND: Conduct disorder (CD), alcohol dependence (AD), and illicit drug dependence (IDD) frequently co-occur. This paper describes the result of an investigation of the extent to which comorbid alcohol and illicit drug dependence in adolescents are explained by etiological factors in common with conduct disorder. METHODS: Participants were 645 MZ twin pairs, 702 DZ twin pairs, 429 biological sibling pairs, and 96 adoptive sibling pairs, aged 12-18 years, from a community based sample. Conduct disorder was measured using the Diagnostic Interview Schedule for Children-IV. Alcohol and illicit drug dependence were assessed using the Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM). For each outcome, subjects were categorized into those with no symptoms, those with one or more symptoms but no diagnosis, and those with a diagnosis. RESULTS: The heritability estimates for CD, AD, and IDD were 58, 66, and 36%, respectively. The genetic correlation between AD and IDD was partially explained by the genetic risk they both share with conduct disorder. CONCLUSIONS: We conclude that conduct disorder in adolescents explains, in part, the co-occurrence of alcohol and illicit drug dependence. Specifically, the genetic contribution to their covariation is explained partially by the genetic contribution in common with conduct disorder.

The need to assess Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) disorders in children younger than 7 years of age has intensified as clinical efforts to diagnose and treat this population have increased, and clinical research on psychopathology has advanced. A new diagnostic instrument for young children was created, the Diagnostic Infant Preschool Assessment (DIPA), and was tested for test-retest reliability and concurrent criterion validity. The caregivers of 50 outpatients aged 1-6 years were interviewed twice by trained interviewers, once by a clinician and once by a research assistant, about eight disorders. The median test-retest intraclass correlation was 0.69, mean 0.61, and values ranged from 0.24 to 0.87. The median test-retest kappa was 0.53, mean 0.52, and values ranged from 0.38 to 0.66. There were no differences by duration between interviews. Concurrent criterion validity show good agreement between the instrument and DSM-based Child Behavior Checklist scales when the DSM-based scales were matched well to the disorder (attention-deficit/hyperactivity inattentive and hyperactive and oppositional disorders). Preliminary data support the DIPA as a reliable and valid measure of symptoms in research and clinical work with very young children. This measure adds a tool that is flexible in covering both DSM-IV syndromes and empirically-validated developmental modifications that can help increase confidence in assessing young children, ensuring coverage of symptoms, and improve access to care.

An alternative formulation of the multigroup common factor model with minimal uniqueness constraints is considered. This alternative formulation is based on a simple identification constraint that is related to the standard maximum likelihood constraint used in single-group common factor analysis. It is argued that the alternative formulation leads to less technical difficulties in applications than earlier formulations of this multigroup common factor model. Furthermore, associated tests for various measurement invariance constraints across groups are proposed, such as an omnibus test for the absence of uniform bias. By means of an empirical example, the fitting of several multigroup common factor models with minimal uniqueness constraints and the testing for measurement invariance over groups are demonstrated. The nesting of multigroup confirmatory factor models under the multigroup common factor model with minimal uniqueness constraints is also discussed. Finally, a small study is performed to investigate the drop in power to detect uniform bias in using the multigroup common factor model with minimal uniqueness constrains instead of a confirmatory special case. The results of the study show a small drop in power under all research conditions.

The authors present a didactic illustration of how item response theory (IRT) can be used to separate measurement bias from true group differences on homogeneous and heterogeneous scales. Several bias detection methods are illustrated with 12 unidimensional Minnesota Multiphasic Personality Inventory (MMPI) factor scales (Waller, 1999) and the 13 multidimensional MMPI validity and clinical scales. The article begins with a brief review of MMPI bias research and nontechnical reviews of the 2-parameter logistic model (2-PLM) and several IRT-based methods for bias detection. A goal of this article is to demonstrate that homogeneous and heterogeneous scales that are composed of biased items do not necessarily yield biased test scores. To that end, the authors perform differential item- and test-functioning analyses on the MMPI factor, validity, and clinical scales using data from 511 Blacks and 1,277 Whites from the California Youth Authority.

BACKGROUND/AIMS: It is relevant to investigate health-related quality of life (HRQOL) in dialysis and chronic kidney disease (CKD) patients in order to optimise treatment. The aim of this study was to investigate HRQOL in dialysis and CKD patients, to compare results from patients treated with hemodialysis (HD) and peritoneal dialysis (PD) and to investigate the prediction of dialysis quality control parameters (blood hemoglobin, plasma albumin, and Kt/V) and tobacco smoking in disease-specific HRQOL. METHODS: Seventy-one HD, 59 PD, and 63 CKD patients participated in the study. Dialysis quality control parameters were measured and the patients completed the questionnaire Kidney Disease Quality Of Life. RESULTS: PD patients rated Dialysis Staff Encouragement and Patient Satisfaction better than HD patients (p< or = 0.05). Dialysis patients scored significant lower than the general population in all generic HRQOL scales (p < or = 0.01), whereas CKD patients scored lower than the general population in 5 of 8 scales (p < or = 0.05). The dialysis quality parameters did not predict dialysis patients' disease specific HRQOL, but tobacco consumption was independently associated with low scores on a number of HRQOL scales. Conclusion: Based on the results, it is suggested to include elements of HRQOL as a supplement to standard quality control parameters. It is also suggested routinely to include information of the beneficial effects of physical activity already in the predialysis program, and to focus on smoking as a very important risk factor.

The rationale underlying factor analysis applies to continuous and categorical variables alike; however, the models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a targeted review and synthesis of the item factor analysis (IFA) estimation literature for ordered-categorical data (e.g., Likert-type response scales) with specific attention paid to the problems of estimating models with many items and many factors. Popular IFA models and estimation methods found in the structural equation modeling and item response theory literatures are presented. Following this presentation, recent developments in the estimation of IFA parameters (e.g., Markov chain Monte Carlo) are discussed. The authors conclude with considerations for future research on IFA, simulated examples, and advice for applied researchers.

There is increasing interest in methods to disentangle the relationship between genotype and (endo)phenotypes in human complex traits. We present a population-based method of increasing the power and cost-efficiency of studies by selecting random individuals with a particular genotype and then assessing the accompanying quantitative phenotypes. Using statistical derivations, power- and cost graphs we show that such a "forward genetics" approach can lead to a marked reduction in sample size and costs. This approach is particularly apt for implementing in epidemiological studies for which DNA is already available but the phenotyping costs are high.

Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms.

The purpose of this article is to inform criminological researchers about tobit regression, an alternative regression model that deserves more attention in this field. Tobit regression is intended for continuous data that are censored, or bounded at a limiting value. The tobit model may be a particularly good match to measures of self-reported offending, provided they have been transformed to reduce skewness. We present empirical analyses that evaluate the match of self-report measures to the assumptions of ordinary least square (OLS) and tobit regression models and that assess the consequences of any violations of assumptions. The analyses use a fourteen-item, self-report measure of delinquency from the Monitoring the Future study, a national survey of high school seniors. These analyses provide clear evidence that (1) transformations to reduce skewness improve the match of OLS to the data but still leave considerable discrepancies, and (2) the tobit model is well suited to the transformed measure. We conclude by assessing the purposes for which tobit offers greater and smaller advantages over OLS regression.

Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques. In the direct approach the ridge estimator is used to fit iteratively the current residuals yielding an alternative to the usual ridge estimator. In partial boosting only part of the regression parameters are reestimated within one step of the iterative procedure. The technique allows to distinguish between variables that are always included in the analysis and variables that are chosen only if relevant. The resulting procedure selects variables in a similar way as the Lasso, yielding a reduced set of influential variables. The suggested procedures are investigated within the classical framework of continuous response variables as well as in the case of generalized linear models. In a simulation study boosting procedures for different stopping criteria are investigated and the performance in terms of prediction and the identification of relevant variables is compared to several competitors as the Lasso and the more recently proposed elastic net. For the evaluation of the identification of relevant variables pseudo ROC curves are introduced.

Although neuroticism has been central to most theories of personality, self-reported neuroticism has had limited success in predicting expected behavioral outcomes. The reason for this may be due, in part, to the imprecision of self-reports. The purpose of this study was to examine the relationship between neural correlates of control systems and neuroticism, extraversion, and self-consciousness. In response to an oddball task, neuroticism was associated with increased dorsal anterior cingulate cortex (dACC) reactivity, typically associated with discrepancy detection, whereas extraversion and self-consciousness were associated with lateral and medial frontoparietal networks, respectively, typically associated with task-focused (lateral) or self-focused (medial) controlled processes. We also examined whether the neural measure of neuroticism would predict a relevant behavioral outcome better than self-reports would. Interoceptive accuracy, an outcome associated with neuroticism, was better accounted for by dACC reactivity (r2 = .74) than by self-reported neuroticism (r2 = .16), suggesting that neural reactivities may provide a more direct measure of personality than self-reports do.

The design of genetic association studies using single-nucleotide polymorphisms (SNPs) requires the selection of subsets of the variants providing high statistical power at a reasonable cost. SNPs must be selected to maximize the probability that a causative mutation is in linkage disequilibrium (LD) with at least one marker genotyped in the study. The HapMap Project performed a genome-wide survey of genetic variation with over 3 million SNPs typed in four populations, providing a rich resource to inform the design of association studies. A number of strategies have been proposed for the selection of SNPs based on observed LD, including construction of metric LD maps and the selection of haplotype-tagging SNPs. Power calculations are important at the study design stage to ensure successful results. Integrating these methods and annotations can be challenging: the algorithms required to implement these methods are complex to deploy, and all the necessary data and annotations are deposited in disparate databases. Here, we review the typical workflows for the selection of markers for association studies utilizing the SNPbrowser software, a freely available, stand-alone application that incorporates the HapMap database together with gene and SNP annotations. Selected SNPs are screened for their conversion potential to genotyping platforms, expediting the set up of genetic studies with an increased probability of success.

The logit and probit models are critical parts of the sociologist's analytical arsenal. We often want to know if a covariate has the same effect for different groups, e.g., men and women. Unfortunately, many attempts to compare the effect of covariates across groups make the unwarranted assumption that each group has the same residual variation. If this assumption is false, comparisons of coefficients can reveal differences where none exist and conceal differences that do exist. Recent work has emphasized the theoretical potential for this problem and proposed a test of whether the effect of covariates differs across groups that is accurate, if limited, despite differences in residual variation. This paper extends these advances in three ways. First, it uses simulations to show that this theoretical problem is substantively significant under a wide range of common conditions, meaning that traditionally executed comparisons of logit coefficients should be viewed skeptically. Second, it uses simulations to assess the power of the test recently proposed to overcome the problem, finding that they are an improvement over naïve comparisons of coefficients, but have significant limitations. Third, it proposes and tests two alternative means of comparing coefficients across groups that avoid the assumption of equal residual variation entirely. The article closes with implications for the practice of research.

OBJECTIVES: We propose the application of a bifactor model for exploring the dimensional structure of an item response matrix, and for handling multidimensionality. BACKGROUND: We argue that a bifactor analysis can complement traditional dimensionality investigations by: (a) providing an evaluation of the distortion that may occur when unidimensional models are fit to multidimensional data, (b) allowing researchers to examine the utility of forming subscales, and, (c) providing an alternative to non-hierarchical multidimensional models for scaling individual differences. METHOD: To demonstrate our arguments, we use responses (N = 1,000 Medicaid recipients) to 16 items in the Consumer Assessment of Healthcare Providers and Systems (CAHPS2.0) survey. ANALYSES: Exploratory and confirmatory factor analytic and item response theory models (unidimensional, multidimensional, and bifactor) were estimated. RESULTS: CAHPS items are consistent with both unidimensional and multidimensional solutions. However, the bifactor model revealed that the overwhelming majority of common variance was due to a general factor. After controlling for the general factor, subscales provided little measurement precision. CONCLUSION: The bifactor model provides a valuable tool for exploring dimensionality related questions. In the Discussion, we describe contexts where a bifactor analysis is most productively used, and we contrast bifactor with multidimensional IRT models (MIRT). We also describe implications of bifactor models for IRT applications, and raise some limitations.

Empirical studies have suggested that two components of health - physical health and mental health - account for the great majority of the variance in health surveys. It has also been suggested that a variety of other indicators can be meaningfully aggregated in order to estimate scores for each of these two health components. Data from the EUROHIS project, the objective of which was to develop common survey instruments for European health surveys, provide an opportunity to further investigate these relationships. The EUROHIS project was led by the WHO Regional Office for Europe and co-sponsored by the BIOMED2 programme of the European Commission. The focus of the current analysis was to study the relationship between the different EUROHIS health indicators and to assess the impact of specific health determinants on physical and mental health outcomes at a cross-cultural level. EUROHIS field-test data for ten countries were used, with a total number of 4849 respondents. Selected variables were tested for their contribution to physical and mental health using multiple hierarchical regression analysis, which took into account the effect of sociodemographic and culture-specific aspects. Modelling was also used to investigate the pathways between the variables. Both the regression and the path analyses revealed conceptually meaningful and statistically powerful explanatory models for the EUROHIS data, in spite of the fact that different sampling frames were used in different countries. Overall, it was not possible to use a combined (single) model that predicted both mental and physical health. These constructs appeared to have different meanings and, furthermore, the interrelationship between mental and physical health appeared to differ in different countries. While the current data need to be carefully interpreted, not least because the EUROHIS field-testing countries used different data collection methods, they offer a rich database for further investigation into cultural aspects that may account for cross-national health differences.

Imaging genetics combines brain imaging and genetics to detect genetic variation in brain structure and function related to behavioral traits, including psychiatric endpoints, cognition, and affective regulation. This special issue features extensive reviews of the current state-of-the-art of the field and adds new findings from twin and candidate gene studies on functional MRI. Here we present a brief overview and discuss a number of desirable future developments which include more specific a priori hypotheses, more standardization of MRI measurements within and across laboratories, and larger sample sizes that allows testing of multiple genes and their interactions up to a scale that allows genetic whole genome association studies. Based on the overall tenet of the contributions to this special issue we predict that imaging genetics will increasingly impact on the classification systems for psychiatric disorders and the early detection and treatment of vulnerable individuals.

This study examines the relationship between personality and quality of life (QOL) in psychiatric outpatients (N=495). Personality was conceptualized using two-dimensional models, respectively, the five-factor model (FFM) and Cloninger's seven-factor model. The WHOQOL-100 was used for assessing QOL. Neuroticism and Harm Avoidance had negative correlations with QOL, whereas Extraversion, Conscientiousness and Self-Directedness correlated positively with QOL. A considerable part of the QOL variance was explained by personality; Cloninger's character factors were superior to the FFM domains. Although not fully comparable, in general our findings are in accordance with earlier studies. Therefore, paying attention to personality and temperament is recommended in future diagnostic procedures, treatment policies, and program evaluations.

The multidisciplinary UCSF Pharmacogenetics of Membrane Transporters project seeks to systematically identify sequence variants in transporters and to determine the functional significance of these variants through evaluation of relevant cellular and clinical phenotypes. The project is structured around four interacting cores: genomics, cellular phenotyping, clinical phenotyping, and bioinformatics. The bioinformatics core is responsible for collecting, storing, and analyzing the information obtained by the other cores and for presenting the results, in particular, for the genomic data. Most of this process is automated using locally developed software written in Python, an open source language well suited for rapid, modular development that meets requirements that are themselves constantly evolving. Here we present the details of transforming ABI trace file data into useful information for project investigators and a description of the types of data analysis and display that we have developed.

We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and South America. These data were analyzed jointly with similar data available in 54 other indigenous populations worldwide, including an additional five Native American groups. The Native American populations have lower genetic diversity and greater differentiation than populations from other continental regions. We observe gradients both of decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic similarity to Siberians-signals of the southward dispersal of human populations from the northwestern tip of the Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic classification of populations. These findings offer new insights into the process of population dispersal and differentiation during the peopling of the Americas.

In this paper we provide a model of interviewer-respondent interaction in survey interviews. Our model is primarily focused on the occurrence of problems within this interaction that seem likely to affect data quality. Both conversational principles and cognitive processes, especially where they do not match the requirements of the respondent's task, are assumed to affect the course of interactions. The cognitive processes involved in answering a survey question are usually described by means of four steps: interpretation, retrieval, judgement and formatting. Each of these steps may be responsible for different overt problems, such as requests for clarification or inadequate answers. Such problems are likely to affect the course of the interaction through conversational principles which may cause, for example, suggestive behaviour on the part of the interviewer, which may in turn yield new problematic behaviours. However, the respondent may not be the only one who experiences cognitive problems; the interviewer may also have such problems, for example with respect to explaining question meaning to the respondent. Thus the model proposed here, unlike most of the other models which concentrate on the respondent, tries to incorporate cognitive processes and conversational principles with respect to both interviewer and respondent. In particular, the model looks at how cognitive processes and conversational principles affect both the interaction between interview participants and the quality of the eventual answers.

Catechol-O-methyltransferase (COMT) has been shown to be critical for prefrontal dopamine flux, prefrontal cortex-dependent cognition and activation. Several potentially functional variants in the gene have been identified, but considerable controversy exists regarding the contribution of individual alleles and haplotypes to risk for schizophrenia, partly because clinical phenotypes are ill-defined and preclinical studies are limited by lack of adequate models. Here, we propose a neuroimaging approach to overcome these limitations by characterizing the functional impact of ambiguous haplotypes on a neural system-level intermediate phenotype in humans. Studying 126 healthy control subjects during a working-memory paradigm, we find that a previously described risk variant in a functional Val158Met (rs4680) polymorphism interacts with a P2 promoter region SNP (rs2097603) and an SNP in the 3' region (rs165599) in predicting inefficient prefrontal working memory response. We report evidence that the nonlinear response of prefrontal neurons to dopaminergic stimulation is a neural mechanism underlying these nonadditive genetic effects. This work provides an in vivo approach to functional validation in brain of the biological impact of complex genetic variations within a gene that may be critical for its clinical association.

A componential model capable of representing simple and complex forms of mental addition was proposed and then tested by using chronometric techniques. A sample of 23 undergraduate students responded to 800 addition problems in a true-false reaction time paradigm. The 800 problems comprised 200 problems of each of four types: two single-digit addends, one single- and one double-digit addend, two double-digit addends, and three single-digit addends. The results revealed that the columnwise product of addends, a structural variable consistent with a memory network retrieval process, was the best predictor of mental addition for each of the four types of problem. Importantly, the componential model allowed estimation of effects of several other structural variables, e.g., carrying to the next column and speed of encoding of digits. High levels of explained variance verified the power of the model to represent the reaction time data, and the stability of estimates across types of problem implied consistent component use by subjects. Implications for research on mental addition are discussed.

This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine fea- tures of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled in- cluding ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadra- ture in gllamm, a freely available program running in Stata.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common and highly heritable disorder of childhood characterized by inattention, hyperactivity and impulsivity. Molecular genetic and pharmacological studies suggest the involvement of the dopaminergic, serotonergic and noradrenergic neurotransmitter systems in the pathogenesis of ADHD. Monoamine oxidase A (MAO-A) encodes an enzyme that degrades biogenic amines, including neurotransmitters such as norepinephrine, dopamine and serotonin. In this study we examined a 30 bp promoter variable number tandem repeat (VNTR) and a functional G/T single nucleotide polymorphism (SNP) at position 941 in exon 8 (941G/T) of MAO-A for association with ADHD in a Taiwanese sample of 212 ADHD probands. METHODS: Within-family transmission disequilibrium test (TDT) was used to analyse association of MAO-A polymorphisms with ADHD in a Taiwanese population. RESULTS: A nominally significant association was found between the G-allele of 941G/T in MAO-A and ADHD (TDT: P = 0.034. OR = 1.57). Haplotype analysis identified increased transmission of a haplotype consisting of the 3-repeat allele of the promoter VNTR and the G-allele of the 941G/T SNP (P = 0.045) to ADHD cases which the strong association with the G-allele drove. CONCLUSION: These findings suggest the importance of the 941G/T MAO-A polymorphism in the development of ADHD in the Taiwanese population. These results replicate previously published findings in a Caucasian sample.

Late-onset Alzheimer's disease is a common complex disorder of old age. Though these types of disorders can be highly heritable, they differ from single-gene (Mendelian) diseases in that their causes are often multifactorial with both genetic and environmental components. Genetic risk factors that have been firmly implicated in the cause are mutations in the amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) genes, which are found in large multi-generational families with an autosomal dominant pattern of disease inheritance, the apolipoprotein E (APOE)epsilon4 allele and the sortilin-related receptor (SORL1) gene. Environmental factors that have been associated with late-onset Alzheimer's disease include depressive illness, various vascular risk factors, level of education, head trauma and estrogen replacement therapy. This complexity may help explain their high prevalence from an evolutionary perspective, but the etiologic complexity makes identification of disease-related genes much more difficult. The "endophenotype" approach is an alternative method for measuring phenotypic variation that may facilitate the identification of susceptibility genes for complexly inherited traits. The usefulness of endophenotypes in genetic analyses of normal brain morphology and, in particular for Alzheimer's disease will be reviewed as will the implications of these findings for models of disease causation. Given that the pathways from genotypes to end-stage phenotypes are circuitous at best, identifying endophenotypes more proximal to the effects of genetic variation may expedite the attempts to link genetic variants to disorders.

Variation databases promise to enable the assessment of recent selec- tion pressure on genomic sequence elements. Evidence of recent selection would suggest recent functional relevance of elements potentially impor- tant for understanding the organization of the human genome and result- ing complex phenotypes and diseases. In contrast, comparative sequence analysis can be employed to focus on those regions that have have been under long-term stabilizing selection. Previously observed biases in vari- ation databases appear to have been reduced to the point that their data can now be used to for investigate the relationships of long-term sequence preservation and recent selective pressures. In a genome-wide study, we identified phylogenetic footprints (PFs) in the vicinity of human genes. In agreement with the distribution of known regulatory sites, the density of these PFs was highest within two thousand base pairs upstream and downstream of genes. Stabilizing selection acting on these PFs was most strongly indicated by significantly reduced single nucleotide polymorphism (SNP) density. Weak correlation between SNP densities of PFs and coding sequences suggests that gene regulation and function often evolve independently. Decreasing diversity in human genes with increasing time of conservation suggests that most old genes have not ceased to be functionally important today. On average, intergenic sequences are under the least selection pressure in the vicinity of genes and may serve as a preferred model for estimating neutral evolution. Intriguingly, we observed increased SNP densities in coding sequences and introns as compared to non-coding sequences of genes conserved only among primates. These genes appear to be mainly involved in the reg- ulation of gene expression and raise the question about mechanisms for adaptive evolution and their role in primate development.

DNA pooling is a cost-effective approach for collecting information on marker allele frequency in genetic studies. It is often suggested as a screening tool to identify a subset of candidate markers from a very large number of markers to be followed up by more accurate and informative individual genotyping. In this article, we investigate several statistical properties and design issues related to this two-stage design, including the selection of the candidate markers for second-stage analysis, statistical power of this design, and the probability that truly disease-associated markers are ranked among the top after second-stage analysis. We have derived analytical results on the proportion of markers to be selected for second-stage analysis. For example, to detect disease-associated markers with an allele frequency difference of 0.05 between the cases and controls through an initial sample of 1000 cases and 1000 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% of the markers should be selected. For the statistical power to identify disease-associated markers, we find that the measurement errors associated with DNA pooling have little effect on its power. This is in contrast to the one-stage pooling scheme where measurement errors may have large effect on statistical power. As for the probability that the disease-associated markers are ranked among the top in the second stage, we show that there is a high probability that at least one disease-associated marker is ranked among the top when the allele frequency differences between the cases and controls are not <0.05 for reasonably large sample sizes, even though the errors associated with DNA pooling in the first stage are not small. Therefore, the two-stage design with DNA pooling as a screening tool offers an efficient strategy in genomewide association studies, even when the measurement errors associated with DNA pooling are nonnegligible. For any disease model, we find that all the statistical results essentially depend on the population allele frequency and the allele frequency differences between the cases and controls at the disease-associated markers. The general conclusions hold whether the second stage uses an entirely independent sample or includes both the samples used in the first stage and an independent set of samples.

Alexithymia refers to the difficulties an individual has in experiencing and expressing feelings. The twenty-item Toronto Alexithymia Scale (TAS-20) and the Bermond-Vorst Alexithymia Questionnaire (BVAQ) with two parallel versions of 20 items have been constructed to measure it. The present study compared the psychometric properties of these two self-report questionnaires in English (N = 290) and French (N = 322) language versions. Confirmatory factor analysis was used to examine the fit between the hypothesised factors and the data. Results revealed a better fit to the data for the second version of the BVAQ (BVAQ-20 B) for both language versions as compared to the TAS-20, the whole BVAQ, or the first version of the BVAQ (BVAQ-20 A). Additionally, the factor comparison of both language versions indicated that only the factorial structure of the BVAQ-20 B was replicable across languages. Concurrent validity of the questionnaires is discussed.

Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 wk of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between (2)H(2)O incorporation into islet DNA in vivo and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including Igf2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation.

G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Win- dows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and ␣2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

It is common practice in IRT to consider items as fixed and persons as random. Both, continuous and categorical person parameters are most often random variables, whereas for items only continuous parameters are used and they are commonly of the fixed type, although exceptions occur. It is shown in the present article that random item parameters make sense theoretically, and that in practice the random item approach is promising to handle several issues, such as the measurement of persons, the explanation of item difficulties, and trouble shooting with respect to DIF. In correspondence with these issues, three parts are included. All three rely on the Rasch model as the simplest model to study, and the same data set is used for all applications. First, it is shown that the Rasch model with fixed persons and random items is an interesting measurement model, both, in theory, and for its goodness of fit. Second, the linear logistic test model with an error term is introduced, so that the explanation of the item difficulties based on the item properties does not need to be perfect. Finally, two more models are presented: the random item profile model (RIP) and the random item mixture model (RIM). In the RIP, DIF is not considered a discrete phenomenon, and when a robust regression approach based on the RIP difficulties is applied, quite good DIF identification results are obtained. In the RIM, no prior anchor sets are defined, but instead a latent DIF class of items is used, so that posterior anchoring is realized (anchoring based on the item mixture). It is shown that both approaches are promising for the identification of DIF.

Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org).

Although both research fields share the term quality of life (QOL), the research on environmental quality of life (EQOL) and health related quality of life (HRQOL) has been completely separated from each other and use the term in a different way. Thus, the choice of instruments used in HRQOL may not easily fit in a project that requires the integration of instruments targeting a common construct of QOL. Necessarily, the conceptual basis of the available instruments has to be evaluated beforehand. This paper discusses the problems surrounding conceptual issues in the selection and use of indicators to measure QOL in environmental and health related studies. Possible means to resolve these conceptual difficulties are outlined. The QOL models of Sen and Lindström are identified as possible sources for that purpose. The conceptual basis of the SF-36 and the WHOQOL are described and checked for their suitability. One practical example of both integrated use of model and instruments in environmental health is presented. Finally, some minimal content requirements are derived. Instruments should cover: main domains and facets of health, contain a broad range of health expressions including positive health, be sensitive enough for the research question and indicators measured subjectively and objectively.

BACKGROUND: Many critical maturational processes take place in the human brain during postnatal development. In particular, the prefrontal cortex does not reach maturation until late adolescence and this stage is associated with substantial white matter volume increases. Patients with schizophrenia and other major psychiatric disorders tend to first present with overt symptoms during late adolescence/early adulthood and it has been proposed that this developmental stage represents a "window of vulnerability". METHODS: In this study we used whole genome microarrays to measure gene expression in post mortem prefrontal cortex tissue from human individuals ranging in age from 0 to 49 years. To identify genes specifically altered in the late adolescent period, we applied a template matching procedure. Genes were identified which showed a significant correlation to a template showing a peak of expression between ages 15 and 25. RESULTS: Approximately 2000 genes displayed an expression pattern that was significantly correlated (positively or negatively) with the template. In the majority of cases, these genes in fact reached a plateau during adolescence with only subtle changes thereafter. These include a number of genes previously associated with schizophrenia including the susceptibility gene neuregulin 1 (NRG1). Functional profiling revealed peak expression in late adolescence for genes associated with energy metabolism and protein and lipid synthesis, together with decreases for genes involved in glutamate and neuropeptide signalling and neuronal development/plasticity. Strikingly, eight myelin-related genes previously found decreased in schizophrenia brain tissue showed a peak in their expression levels in late adolescence, while the single myelin gene reported increased in patients with schizophrenia was decreased in late adolescence. CONCLUSION: The observed changes imply that molecular mechanisms critical for adolescent brain development are disturbed in schizophrenia patients.

There is now a large body of evidence that supports the conclusion that individual differences in most, if not all, reliably measured psychological traits, normal and abnormal, are substantively influenced by genetic factors. This fact has important implications for research and theory building in psychology, as evidence of genetic influence unleashes a cascade of questions regarding the sources of variance in such traits. A brief list of those questions is provided, and representative findings regarding genetic and environmental influences are presented for the domains of personality, intelligence, psycho- logical interests, psychiatric illnesses, and social attitudes. These findings are consistent with those reported for the traits of other species and for many human physical traits, suggesting that they may represent a general biological phenomenon.

Multinomial logistic regression provides the standard penalised maximum- likelihood solution to multi-class pattern recognition problems. More recently, the development of sparse multinomial logistic regression models has found ap- plication in text processing and microarray classification, where explicit identifi- cation of the most informative features is of value. In this paper, we propose a sparse multinomial logistic regression method, in which the sparsity arises from the use of a Laplace prior, but where the usual regularisation parameter is inte- grated out analytically. Evaluation over a range of benchmark datasets reveals this approach results in similar generalisation performance to that obtained using cross-validation, but at greatly reduced computational expense.

Detecting the association between genetic markers and complex diseases can be a critical first step toward identification of the genetic basis of disease. Misleading associations can be avoided by choosing as controls the parents of diseased cases, but the availability of parents often limits this design to early-onset disease. Alternatively, sib controls offer a valid design. A general multivariate score statistic is presented, to detect the association between a multiallelic genetic marker locus and affection status; this general approach is applicable to designs that use parents as controls, sibs as controls, or even unrelated controls whose genotypes do not fit Hardy-Weinberg proportions or that pool any combination of these different designs. The benefit of this multivariate score statistic is that it will tend to be the most powerful method when multiple marker alleles are associated with affection status. To plan these types of studies, we present methods to compute sample size and power, allowing for varying sibship sizes, ascertainment criteria, and genetic models of risk. The results indicate that sib controls have less power than parental controls and that the power of sib controls can be increased by increasing either the number of affected sibs per sibship or the number of unaffected control sibs. The sample-size results indicate that the use of sib controls to test for associations, by use of either a single-marker locus or a genomewide screen, will be feasible for markers that have a dominant effect and for common alleles having a recessive effect. The results presented will be useful for investigators planning studies using sibs as controls.

BACKGROUND AND OBJECTIVE: This study assesses the extent to which the RAND-36/SF-36 items measure physical and mental health (PH and MH), as implied by existing summary scoring systems. METHODS: A total of 1,714 heterogeneous cancer and HIV/AIDS patients were recruited from five institutions. Of these, 56% were women; 81% Caucasians; and about 10% were from each of the major cancer types and HIV/AIDS. RESULTS: Analyses of the SF-36 confirmed the two dimensions of health namely physical and mental. However, item fit statistics and residual factor analysis revealed that some items intended to represent the PH dimension fit better with the MH dimension. CONCLUSION: This paper demonstrated the value of Rasch residual factor analysis for understanding and enhancing interpretation of health.

Simultaneously testing a collection of null hypotheses about a data generating distribution based on a sample of independent and identically distributed observations is a fundamental and important statistical problem involving many applications. Methods based on marginal null distributions (i.e., marginal p-values) are attractive since the marginal p-values can be based on a user supplied choice of marginal null distributions and they are computationally trivial, but they, by necessity, are known to either be conservative or to rely on assumptions about the dependence structure between the test-statistics. Re-sampling based multiple testing (Westfall and Young, 1993) involves sampling from a joint null distribution of the test-statistics, and controlling (possibly in a, for example, step-down fashion) the user supplied type-I error rate under this joint null distribution for the test-statistics. A generally asymptotically valid null distribution avoiding the need for the subset pivotality condition for the vector of test-statistics was proposed in Pollard, van der Laan (2003) for null hypotheses about general real valued parameters. This null distribution was generalized in Dudoit, vanderLaan, Pollard (2004) to general null hypotheses and test-statistics. In ongoing recent work van der Laan, Hubbard (2005), we propose a new generally asymptotically valid null distribution for the test-statistics and a corresponding bootstrap estimate, whose marginal distributions are user supplied, and can thus be set equal to the (most powerful) marginal null distributions one would use in univariate testing to obtain a p-value. Previous proposed null distributions either relied on a restrictive subset pivotality condition (Westfall and Young) or did not guarantee this latter property (Dudoit, vanderLaan, Pollard, 2004). It is argued and illustrated that the resulting new re-sampling based multiple testing methods provide more accurate control of the wished Type-I error in finite samples and are more powerful. We establish formal results and investigate the practical performance of this methodology in a simulation and data analysis.

There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.

In this paper we show how Perl, an expressive and extensible high-level programming language, with network and object-oriented programming support, can be used in process- ing data for statistics and statistical computing. The paper is organized in two parts. In Part I, we introduce the Perl programming language, with particular emphasis on the fea- tures that distinguish it from conventional languages. Then, using practical examples, we demonstrate how Perl's distinguishing features make it particularly well suited to perform labor intensive and sophisticated tasks ranging from the preparation of data to the writing of statistical reports. In Part II we show how Perl can be extended to perform statistical computations using modules and by “embedding” specialized statistical applications. We provide example on how Perl can be used to do simple statistical analyses, perform com- plex statistical computations involving matrix algebra and numerical optimization, and make statistical computations more easily reproducible. We also investigate the numerical and statistical reliability of various Perl statistical modules. Important computing issues such as ease of use, speed of calculation, and efficient memory usage, are also considered.

FAMHAP is an established software for haplotype association analysis of nuclear families. We have released a major update that comprises various new features for case-control data. Furthermore, weprovide an additional program runFamhap that allows users to start the same method repeatedly for varying sets of genetic markers. In addition, a platform-independent graphical user interface (GUI) was developed to simplify the usage of both FAMHAP and runFamhap. The runFamhap program greatly facilitates the application of FAMHAP to genome-wide association studies (GWAS) and supports flexible genome-wide haplotype analysis. As an example, we describe application to HapMap data. AVAILABILITY: The software is available at http://famhap.meb.uni-bonn.de

Survival and functional outcomes for lung transplant recipients continue to lag behind those for heart recipients. Whether these poorer physical outcomes translate into poorer quality of life (QOL) for lung recipients relative to heart recipients is unknown. Lung versus heart transplant recipients' perceptions of QOL were longitudinally compared at three time-points across the first year posttransplant. Additionally, potentially important predictors of patient QOL were examined. Adult transplant recipients (N = 199) participated in semi-structured interviews that included measures of QOL, optimism, mastery, social support, religiosity and coping. Temporal patterns of QOL change were compared between lung and heart recipients who survived until 1 year posttransplant using mixed-model, hierarchical analysis of variance (ANOVA). Demographic and psychosocial predictors were examined with multiple regression analysis to identify the unique effects of each variable on QOL 1 year posttransplant. While heart recipients' QOL across several domains was higher shortly after transplant, lung patients' QOL improved and was equivalent to that of heart recipients by 1 year posttransplant. Greater optimism and support from friends predicted better QOL in physical, psychological and social domains. Conversely, avoidant coping strategies predicted poorer physical functioning. Thus, while clinical interventions designed to improve QOL posttransplant should be tailored to transplant recipients' initial psychosocial assets and liabilities, they need not be distinguished by transplant type.

Two psychometric models are presented for evaluating the difficulty of the distractors in multiple-choice items. They are based on the criterion of rising distractor selection ratios, which facilitates interpretation of the subject and item parameters. Statistical inferential tools are developed in a Bayesian framework: modal a posteriori estimation by application of an EM algorithm and model evaluation by monitoring posterior predictive replications of the data matrix. An educational example with real data is included to exemplify the application of the models and compare them with the nominal categories model.

This study examines the improvement in validity coefficients and structural relationships derived from formally modeling latent variables of adverse alcohol-related constructs. Results of a confirmatory factor analysis of responses from 8,755 participants in the 1988 National Household Survey of Drug Abuse support a three-factor model of alcohol abuse, dependence, and adverse consequences. A comparison of construct intercorrelations shows latent variables to be superior to measurement approaches using unweighted sums, quantity x frequency calculation, and single-item measurement.

Cognitive theories of numerical representation suggest that understanding of numerical quantities is driven by a magnitude representation associated with the intraparietal sulcus and possibly under genetic control. The aim of this study was to investigate, using fMRI and structural imaging, the interaction between the abnormal development of numerical representation in an X-linked condition, Turner syndrome (TS), and the development of the intraparietal sulcus. fMRI during exact and approximate calculation in TS showed an abnormal modulation of intraparietal activations as a function of number size. Morphological analysis revealed an abnormal length, depth, and sulcal geometry of the right intraparietal sulcus, suggesting an important disorganization of this region in TS. Thus, a genetic form of developmental dyscalculia can be related to both functional and structural anomalies of the right intraparietal sulcus, suggesting a crucial role of this region in the development of arithmetic abilities.

BACKGROUND: Inhalant use is among the most pernicious and poorly understood forms of adolescent substance use. Many youth in the juvenile justice system have used inhalants, but little is known about inhalant use disorders (IUDs) in antisocial youth populations. The purpose of this study was to examine the prevalence, clinical features, and latent structure of DSM-IV IUDs in a state population of antisocial youth. METHODS: Cross-sectional survey conducted in 2003. Of 740 youth residing in Missouri State Division of Youth Services' (MDYS) residential treatment facilities at the time the study was conducted, 723 (97.7%) completed interviews. Eighty-seven percent were male, with a mean age of 15.5 (SD = 1.2). Nearly 4 in 10 youth (38.5%; n = 279) reported lifetime inhalant use. Youth ranged from very mildly to severely antisocial. RESULTS: Of 279 inhalant users, 52 (18.6%) met DSM-IV inhalant abuse criteria and 79 (28.3%) met inhalant dependence criteria. Five of 10 IUD criteria were met by > 10% of the total sample. Latent class analyses demonstrated a substantial concordance between DSM-IV-defined IUDs and an empirically-derived classification based on responses to DSM-IV IUD diagnostic criteria. CONCLUSION: IUDs and constituent criteria were prevalent among youth in the juvenile justice system. Two groups of problem inhalant users were identified, symptomatic users-DSM-IV inhalant abuse and highly symptomatic users-DSM-IV inhalant dependence, which differed primarily in severity of inhalant-related problems. Inhalant screening, prevention and treatment efforts in juvenile justice settings are rarely delivered, but critically needed.

ABSTRACT Objective: To review the efficacy of instrumental variable (IV) models in addressing a variety of assumption violations to ensure standard ordinary least squares (OLS) estimates are consistent. IV models gained popularity in outcomes research because of their ability to consistently estimate the average causal effects even in the presence of unmeasured confounding. However, in order for this consistent estimation to be achieved, several conditions must hold. In this article, we provide an overview of the IV approach, examine possible tests to check the prerequisite conditions, and illustrate how weak instruments may produce inconsistent and inefficient results. Methods: We use two IVs and apply Shea's partial R-square method, the Anderson canonical correlation, and Cragg-Donald tests to check for weak instruments. Hall-Peixe tests are applied to see if any of these instruments are redundant in the analysis. Results: A total of 14,952 asthma patients from the MarketScan Commercial Claims and Encounters Database were examined in this study. Patient health care was provided under a variety of fee-for-service, fully capitated, and partially capitated health plans, including preferred provider organizations, point of service plans, indemnity plans, and health maintenance organizations. We used controller-reliever copay ratio and physician practice/prescribing patterns as an instrument. We demonstrated that the former was a weak and redundant instrument producing inconsistent and inefficient estimates of the effect of treatment. The results were worse than the results from standard regression analysis. Conclusion: Despite the obvious benefit of IV models, the method should not be used blindly. Several strong conditions are required for these models to work, and each of them should be tested. Otherwise, bias and precision of the results will be statistically worse than the results achieved by simply using standard OLS.

Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical model treats the ordering of the members of a pair as random. Because the matrix has only one estimate of a population variance and covariance, both the observed matrix and the residual matrix from a fitted model are easy to inspect visually; there is no need to mentally average homologous statistics. Fitting a model to the intraclass matrix also gives the same log likelihood, likelihood-ratio (LR) chi2, and parameter estimates as fitting that model to the raw data. A major advantage of the intraclass matrix is that only two factors influence the LR chi2-the sampling error in estimating population parameters and the discrepancy between the model and the observed statistics. The more frequently used interclass covariance matrix adds a third factor to the chi2-sampling error of homologous statistics. Because of this, the degrees of freedom for fitting models to an intraclass matrix differ from fitting that model to an interclass matrix. Future research is needed to establish differences in power-if any-between the interclass and the intraclass matrix.

Bayesian modification indices are presented that provide information for the process of model evaluation and model modification. These indices can be used to investigate the improvement in a model if fixed parameters are re-specified as free parameters. The indices can be seen as a Bayesian analogue to the modification indices commonly used in a frequentist framework. The aim is to provide diagnostic information for multi-parameter models where the number of possible model violations and the related number of alternative models is too large to render estimation of each alternative practical. As an example, the method is applied to an item response theory (IRT) model, that is, to the two-parameter model. The method is used to investigate differential item functioning and violations of the assump- tion of local independence.

Consider the multiple testing problem of testing null hypotheses $H_1,...,H_s$. A classical approach to dealing with the multiplicity problem is to restrict attention to procedures that control the familywise error rate ($FWER$), the probability of even one false rejection. But if $s$ is large, control of the $FWER$ is so stringent that the ability of a procedure that controls the $FWER$ to detect false null hypotheses is limited. It is therefore desirable to consider other measures of error control. This article considers two generalizations of the $FWER$. The first is the $k-FWER$, in which one is willing to tolerate $k$ or more false rejections for some fixed $k>=1$. The second is based on the false discovery proportion ($FDP$), defined to be the number of false rejections divided by the total number of rejections (and defined to be 0 if there are no rejections). Benjamini and Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289-300] proposed control of the false discovery rate ($FDR$), by which they meant that, for fixed $α$, $E(FDP)<=α$. Here, we consider control of the $FDP$ in the sense that, for fixed $γ$ and $α$, $P{FDP>γ}<=α$. Beginning with any nondecreasing sequence of constants and $p$-values for the individual tests, we derive stepup procedures that control each of these two measures of error control without imposing any assumptions on the dependence structure of the $p$-values. We use our results to point out a few interesting connections with some closely related stepdown procedures. We then compare and contrast two $FDP$-controlling procedures obtained using our results with the stepup procedure for control of the $FDR$ of Benjamini and Yekutieli [Ann. Statist. 29 (2001) 1165-1188].

Disturbed circadian rhythms have been observed in seasonal affective disorder (SAD). The aim of this study was to further investigate this connection, and to test for potential association between polymorphisms in circadian clock-related genes and SAD, seasonality (seasonal variations in mood and behavior), or diurnal preference (morningness-eveningness tendencies). A total of 159 European SAD patients and 159 matched controls were included in the genetic analysis, and subsets were screened for seasonality (n=177) and diurnal preference (n=92). We found that diurnal preference was associated with both SAD and seasonality, supporting the hypothesis of a link between circadian rhythms and seasonal depression. The complete case-control material was genotyped for polymorphisms in the CLOCK, Period2, Period3, and NPAS2 genes. A significant difference between patients and controls was found for NPAS2 471 Leu/Ser (chi(2)=9.90, Bonferroni corrected P=0.035), indicating a recessive effect of the leucine allele on disease susceptibility (chi(2)=6.61, Bonferroni corrected P=0.050). Period3 647 Val/Gly was associated with self-reported morningness-eveningness scores (n=92, one-way ANOVA: F=4.99, Bonferroni corrected P=0.044), with higher scores found in individuals with at least one glycine allele (t=3.1, Bonferroni corrected P=0.013). A second, population-based sample of individuals selected for high (n=127) or low (n=98) degrees of seasonality, was also genotyped for NPAS2 471 Leu/Ser. There was no significant difference between these seasonality extreme groups, and none of the polymorphisms studied were associated with seasonality in the SAD case-control material (n=177). In conclusion, our results suggest involvement of circadian clock-related polymorphisms both in susceptibility to SAD and diurnal preference.

Indexes developed to measure physical functioning as an essential component of general health status are often based on sets of hierarchically-structured items intended to represent a broad underlying concept. Rasch Item Response Theory (IRT) provides a methodology to examine the hierarchical structure, unidimensionality, and reproducibility of item positions (calibrations) along a scale. Data gathered on the 10-item Physical Functioning Scale (PF-10) from a large sample of Medical Outcomes Study patients (N = 3445) were used to examine the hierarchical order, unidimensionality, and reproducibility of item calibrations. Rasch-IRT analyses generated an empirical item hierarchy, confirmed the unidimensionality of the PF-10 for most patients, and established the reproducibility of item calibrations across patient populations and repeated tests. These findings support the content validity of the PF-10 as a measure of physical functioning and suggest that valid Rasch-IRT summary scores could be generated as an alternative to the current Likert summative scores. Unidimensionality and reproducibility of the item scale are essential prerequisites for the development of Rasch-based person measures of physical functioning that can be used across populations and over repeated tests.

Impulsivity is a personality trait exhibited by healthy individuals, but excessive impulsivity is associated with some mental disorders. Lesion and functional neuroimaging studies indicate that the ventromedial prefrontal region (VMPFC), including the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC) and medial prefrontal cortex, and the amygdala may modulate impulsivity and aggression. However, no morphometric study has examined the association between VMPFC and impulsivity. We hypothesized that healthy subjects with high impulsivity would have smaller volumes in these brain regions compared with those with low impulsivity. Sixty-two healthy subjects were studied (age 35.4 +/- 12.1 years) using a 1.5-T MRI system. The Barratt impulsiveness scale (BIS) was used to assess impulsivity. Images were processed using an optimized voxel-based morphometry (VBM) protocol. We calculated the correlations between BIS scale scores and the gray matter (GM) and white matter (WM) volumes of VMPFC and amygdala. GM volumes of the left and right OFC were inversely correlated with the BIS total score (P = 0.04 and 0.02, respectively). Left ACC GM volumes had a tendency to be inversely correlated with the BIS total score (P = 0.05). Right OFC GM volumes were inversely correlated with BIS nonplanning impulsivity, and left OFC GM volumes were inversely correlated with motor impulsivity. There were no significant WM volume correlations with impulsivity. The results of this morphometry study indicate that small OFC volume relate to high impulsivity and extend the prior finding that the VMPFC is involved in the circuit modulating impulsivity.

BACKGROUND/OBJECTIVES: To achieve a consensus, among a panel of experts, on the best clinical criteria for the clinical diagnosis of carpal tunnel syndrome (CTS). METHODS: Experts rated the diagnostic importance of items from the clinical history and physical examination for CTS. The ratings were expressed on a 10-cm visual analog scale. The average and standard deviation of the scores for each item were returned to the panelists. The panel members evaluated the items a second time with knowledge of the group responses from the first round. The scores were standardized to minimize scaling variations and, after the second round, the items were ranked in order of importance assigned by the group. Cronbach's alpha was used as a measure of homogeneity for the rankings. Increasing homogeneity was considered to be an indication of consensus among the panelists. RESULTS: Cronbach's alpha increased from 0.86 after the first round to 0.91 after the second iteration. Panelists who were relative outliers on the first round demonstrated a much higher correlation with the entire group after the second round. CONCLUSIONS: Delphi is an effective method of establishing consensus for certain clinical questions. Cronbach's alpha was a useful statistic for measuring the extent of consensus among the panel members. Delphi was chosen from the possible methods of group process because of its inherent feasibility. The absence of a need by the panelists to meet in person removed any constraint on the geographic location of the panel members. In addition, the anonymous nature of Delphi was thought to be a key factor in avoiding a result that might be skewed by one or more persuasive panelists. Both of these characteristics were felt to be particularly important to the topic on which consensus was sought, the clinical diagnostic criteria for CTS. This movement in the opinions of some of the panelists appeared to result from the feedback of information describing the group opinion.

There are now about 100 genes known to cause Mendelian inherited cancer syndromes, but these only explain a minor part of the familial clustering of the common cancers. The increased familial relative risk of cancer in the general population must largely involve genes of low- or moderate-penetrance. Until recently, attempts to identify cancer predisposition genes with low penetrance had proved similarly unrewarding. However, in the past 2 years, developments in this area have been rapid. In particular, the 'common disease-common variant' model of predisposition has come to the fore. In this model, alleles of high frequency (typically > 10%) and low penetrance (typically < two-fold increased lifetime risk) contribute substantially to susceptibility to the common human diseases, including cancers. Many common risk alleles for cancer have been found by genome-wide association studies (GWASs) in the form of tagging SNPs, although identification of the disease-causing variants generally remains a difficult problem. The 'common disease-common variant' model has recently been criticized by proponents of a 'common disease-rare variant' model. In fact, the conflict between the models is false and a more continuous approach, bounded only by technical limitations and sample sizes, appears to be more appropriate. In this review, we summarize the general findings from cancer GWASs and their problems, and discuss the issues of finding rarer variants and other forms of cancer-predisposing variation, such as copy number polymorphisms.

Summary. Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health-care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, in practice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. Such is the case in the INSTINCT trial designed to investigate the effectiveness of an education program in enhancing the tPA use in stroke patients. In this article, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. A simulation study shows that, under various confounding scenarios, the BMW design can yield substantial reductions in the MSE for the treatment effect estimator compared to a completely randomized or matched-pair design. The BMW design is also compared with a model-based approach adjusting for the estimated propensity score and Robins-Mark-Newey E-estimation procedure in terms of efficiency and robustness of the treatment effect estimator. These investigations suggest that the BMW design is more robust and usually, although not always, more efficient than either of the approaches. The design is also seen to be robust against heterogeneous error. We illustrate these methods in proposing a design for the INSTINCT trial.

BACKGROUND: In order to understand the validity of the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) alcohol abuse and dependence diagnoses, studies are needed in both clinical and general population samples. The purpose of this study was to examine the construct and criterion-oriented validity of DSM-IV alcohol dependence and abuse in the general population with respect to factor structure and their relationship to family history of alcoholism, treatment utilization, and psychiatric comorbidity. METHODS: This analysis is based on data from the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), in which nationally representative data were collected in personal interviews conducted with one randomly selected adult in each sample household or group quarters. A subset (n=26,946) of the NESARC sample (total n=43,093) who reported drinking one or more drinks during the year preceding the interview formed the basis of analyses. Latent variable modeling was used to assess the concurrent validity of DSM-IV alcohol abuse and dependence symptom items. RESULTS: The latent variable modeling yielded one major factor related to alcohol dependence, a second factor related to alcohol abuse and a third smaller factor defined by tolerance. The validity of alcohol dependence in general population samples was further supported by statistically significant associations with family history of alcoholism, treatment utilization, and psychiatric and medical comorbidities. CONCLUSIONS: The factor structure and relationship to external criterion variables observed in the study provide support for the further validity of DSM-IV alcohol dependence in the general population, whereas support for the validity of DSM-IV abuse was equivocal.

A method is described for the determination of a measure of relative risk from vital statistical data. If the frequency of disease in a population is linearly related to the level of exposure to a given factor, then a measure of the relative risk can be estimated from the slope and intercept of the regression line. For example, when the exposure is measured in terms of the proportion of the population exposed to the factor, then the relative risk is equal to [Formula: see text] This offers an indirect but simple and inexpensive method for estimating relative risk. It should be used with caution, particularly where confounding factors may be responsible for the apparent association between disease and factor. Applications of the method to estimate the relative risk of (a) circulatory diseases in women using oral contraceptives and (b) ovarian cancer in women with different average family sizes, both yielded relative risk estimates comparable with those obtained from case-control and prospective studies.

Latent class models (LCMs) can be used to assess diagnostic test performance when no reference test (a gold standard) is available, considering two latent classes representing disease or non-disease status. One of the basic assumptions in such models is that of local or conditional independence: all indicator variables (tests) are statistically independent within each latent class. However, in practice this assumption is often violated; hence, the two-LCM fits the data poorly. In this paper, we propose the use of Biplot methods to identify the conditional dependence between pairs of manifest variables within each latent class. Additionally, we propose incorporating such dependence in the corresponding latent class using the log-linear formulation of the model.

Specification of a null hypothesis is central to the interpretation of types and an- titypes in Configural Frequency Analysis (CFA). When the observed frequency of a variable category exceeds (or is less than) its corresponding expected frequency, the variable category is considered a type (or antitype). This article attempts to help the researcher to determine whether types or antitypes can be expected to emerge based on power and sample size considerations. Results are presented for the special cases of 2x2 and 2x2x2 tables respectively for First Order CFA.

The Big Five Inventory (BFI) is a self-report measure designed to assess the high-order personality traits of Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. As part of the International Sexuality Description Project, the BFI was translated from English into 28 languages and administered to 17,837 individuals from 56 nations. The resulting cross-cultural data set was used to address three main questions: Does the factor structure of the English BFI fully replicate across cultures? How valid are the BFI trait profiles of individual nations? And how are personality traits distributed throughout the world? The five-dimensional structure was robust across major regions of the world. Trait levels were related in predictable ways to self-esteem, sociosexuality, and national personality profiles. People from the geographic regions of South America and East Asia were significantly different in open- ness from those inhabiting other world regions. The discussion focuses on limitations of the current data set and important directions for future research.

Schizophrenia is a brain disease with differing symptomatic presentations, outcomes, and complex genetic mechanisms. A selection of recent work integrating clinical observations, human brain imaging and genetics will be reviewed. While the mechanics of brain dysfunction in schizophrenia remains to be well understood, the emerging evidence suggests that a number of interacting genetic mechanisms in dopaminergic and glutamatergic systems affect fundamental disease-related cognitive brain processes and may do so early in disease neurodevelopment. The availability of new imaging and genetic technologies, and institutional support for research in the translational neurosciences, extends the hope that increased understanding of these brain processes could yield meaningful clinical applications.

We present a support vector machine (SVM) based framework for DNA segmen- tation into binary classes. Two applications are explored: transcription start site prediction and transcription factor binding prediction. Experiments demonstrate our approach has sig- nificantly better performance than other methods on both tasks.

In the area of statistical limitation, releasing synthetic data sets has become a popular method for limiting the risks of disclosure of sensitive information and at the same time maintaining analytic utility of data. However, less work has been done on how to create synthetic contingency tables that preserve some summary statistics of the original table. Studies in this area have primarily focused on generating replacement tables that preserve the margins of the original table since the latter support statistical inferences for a large set of parametric tests and models. Yet, not all synthetic tables that preserve a set of margins yield consistent results. In this paper, we propose alternative synthetic table releases. We describe how to generate complete two-way contingency tables that have the same set of observed conditional frequencies by using tools from computational algebra. We study both the disclosure risk and the data utility associated with such synthetic tabular data releases, and compare them to the traditionally released synthetic tables.

The paper is aimed to investigate the performance of information criteria in selecting latent class analysis models which are often used in research of phenotype identification. Six information criteria and a sample size adjustment (Psychometrika 52 (1987) 333) are compared under various sample sizes and model dimensionalities. The simulation design is particularly meaningful for phenotypic research in practice. Results show that improvements by the sample size adjustment are considerable. In addition, the sample size and model dimensionality effects are found to be influential in the simulation study.

BACKGROUND: The use of item response theory (IRT) to measure self-reported outcomes has burgeoned in recent years. Perhaps the most important application of IRT is computer-adaptive testing (CAT), a measurement approach in which the selection of items is tailored for each respondent. OBJECTIVE. To provide an introduction to the use of CAT in the measurement of health outcomes, describe several IRT models that can be used as the basis of CAT, and discuss practical issues associated with the use of adaptive scaling in research settings. PRINCIPAL POINTS: The development of a CAT requires several steps that are not required in the development of a traditional measure including identification of "starting" and "stopping" rules. CAT's most attractive advantage is its efficiency. Greater measurement precision can be achieved with fewer items. Disadvantages of CAT include the high cost and level of technical expertise required to develop a CAT. CONCLUSIONS: Researchers, clinicians, and patients benefit from the availability of psychometrically rigorous measures that are not burdensome. CAT outcome measures hold substantial promise in this regard, but their development is not without challenges.

BACKGROUND: Data for health surveys are often collected using either mailed questionnaires, telephone interviews or a combination. Mode of data collection can affect the propensity to refuse to respond and result in different patterns of responses. The objective of this paper is to examine and quantify effects of mode of data collection in health surveys. METHODS: A stratified sample of 4,000 adults residing in Denmark was randomised to mailed questionnaires or computer-assisted telephone interviews. 45 health-related items were analyzed; four concerning behaviour and 41 concerning self assessment. Odds ratios for more positive answers and more frequent use of extreme response categories (both positive and negative) among telephone respondents compared to questionnaire respondents were estimated. Tests were Bonferroni corrected. RESULTS: For the four health behaviour items there were no significant differences in the response patterns. For 32 of the 41 health self assessment items the response pattern was statistically significantly different and extreme response categories were used more frequently among telephone respondents (Median estimated odds ratio: 1.67). For a majority of these mode sensitive items (26/32), a more positive reporting was observed among telephone respondents (Median estimated odds ratio: 1.73). The overall response rate was similar among persons randomly assigned to questionnaires (58.1%) and to telephone interviews (56.2%). A differential nonresponse bias for age and gender was observed. The rate of missing responses was higher for questionnaires (0.73-6.00%) than for telephone interviews (0-0.51%). The "don't know" option was used more often by mail respondents (10-24%) than by telephone respondents (2-4%). CONCLUSION: The mode of data collection affects the reporting of self assessed health items substantially. In epidemiological studies, the method effect may be as large as the effects under investigation. Caution is needed when comparing prevalences across surveys or when studying time trends.

Imaging genetics provides an enormous amount of functional-structural data on gene effects in living brain, but the sheer quantity of potential phenotypes raises concerns about false discovery. Here, we provide the first empirical results on false positive rates in imaging genetics. We analyzed 720 frequent coding SNPs without significant association with schizophrenia and a subset of 492 of these without association with cognitive function. Effects on brain structure (using voxel-based morphometry, VBM) and brain function, using two archival imaging tasks, the n-back working memory task and an emotional face matching task, were studied in whole brain and regions of interest and corrected for multiple comparisons using standard neuroimaging procedures. Since these variants are unlikely to impact relevant brain function, positives obtained provide an upper empirical estimate of the false positive association rate. In a separate analysis, we randomly permuted genotype labels across subjects, removing any true genotype-phenotype association in the data, to derive a lower empirical estimate. At a set correction level of 0.05, in each region of interest and data set used, the rate of positive findings was well below 5% (0.2-4.1%). There was no relationship between the region of interest and the false positive rate. Permutation results were in the same range as empirically derived rates. The observed low rates of positives provide empirical evidence that the type I error rate is well controlled by current commonly used correction procedures in imaging genetics, at least in the context of the imaging paradigms we have used. In fact, our observations indicate that these statistical thresholds are conservative.

A clarified conceptual meaning of depression in individuals with hepatitis C virus infection is proposed based on a critical review of literature. Moving beyond an exclusively biomedical perspective, depression in hepatitis C is explained by a cluster of factors that incorporate physiological, psychological, and social dimensions. Symptom experience, stigma, and uncertainty are factors that span the complex nature of depression in individuals with hepatitis C. This broadened perspective incorporates individual and societal values and beliefs regarding hepatitis C and encompasses the multidimensional complexity of depression in hepatitis C. Hepatitis C-related depression presents an enormous challenge for nurses because of its interference with treatment adherence and significant negative impact on the individual's quality of life. Nursing theorists, researchers, and clinicians may benefit from a clear conceptual understanding of the unique nature of depression in this growing segment of the U.S. population. This clarified conceptual meaning needs to be validated through qualitative, quantitative, and longitudinal studies with this population. It is hoped that future theorists, researchers, and practitioners will contribute to our conceptual understanding, resulting in improved quality of life for this special population.

BACKGROUND: The purpose of this study was to evaluate the reliability and construct validity of the Participation in Life Activities Scale, an instrument designed to measure older school-age child and early adolescent level of involvement in chosen pursuits. METHODS: A cross-sectional design was used. The convenience sample consisted of 313 school-age children and early adolescents with asthma, ages 9-15 years. The self-report summative scale of interest is a 3-indicator survey. Higher scores are reflective of higher levels of participation. Internal consistency reliability and construct validity for the entire sample and sub groups of the sample were evaluated. RESULTS: The instrument was deemed sound for the entire sample as well as sub groups based on sex, race, age, socioeconomic status, and severity of illness. Cronbach's alpha coefficient for internal consistency reliability for the entire sample was .74. Exploratory factor analysis indicated a single component solution (loadings .79-.85) accounting for 66% of the explained variance. Construct validity was established by testing the posed relationship between participation in life activities scores and severity of illness. Confirmatory factor analysis revealed a good fit between the data and specified model, chi2(10, n = 302) = 8.074, p = .62. CONCLUSION: This instrument could be used (a) in clinical settings to diagnose restricted participation in desired activities, guide decision-making about treatment plans to increase participation, and motivate behavioral change in the management of asthma; and (b) in research settings to explore factors influencing and consequences of restricted and unrestricted participation, and as an outcome measure to evaluate the effectiveness of programs designed to foster child and early adolescent management of asthma.

OBJECTIVE: We compared the minimal important difference (MID) with the minimal detectable change (MDC) generated by distribution-based methods. STUDY DESIGN: Studies of two quality-of-life instruments (Chronic Respiratory Questionnaire [CRQ] and Rhinoconjunctivitis Quality of Life Questionnaire [RQLQ]) and two physician-rated disease-activity indices (Pediatric Ulcerative Colitis Activity Index [PUCAI] and Pediatric Crohn's Disease Activity Index [PCDAI]) provided longitudinal data. The MID values were calculated from global ratings of change (small change for CRQ and RQLQ; moderate for PUCAI and PCDAI) using receiver-operating characteristic (ROC) curve and mean change. Results were compared with five distribution-based strategies. RESULTS: Of the methods used to calculate the MDC, the 95% limits of agreement and the reliable change index yielded the largest estimates. In the patient-rated psychometric instruments, 0.5 SD was always greater than 1 standard error of measurements (SEM), and both fell between the mean change and the ROC estimates, on two of four occasions. The reliable change index came closest to MID of moderate change. CONCLUSION: For patient-rated psychometric instruments, 0.5 SD and 1 SEM provide values closest to the anchor-based estimates of MID derived from small change, and the reliable change index for physician-rated clinimetric indices based on moderate change. Lack of consistency across measures suggests that distribution-based approaches should act only as temporary substitutes, pending availability of empirically established anchor-based MID values.

Variable selection for cluster analysis is a dif- ficult problem. The difficulty originates not only from the lack of class information but also the fact that high-dimensional data are often multifaceted and can be meaningfully clustered in multiple ways. In such a case the effort to find one subset of attributes that presumably gives the “best” clustering may be misguided. It makes more sense to facilitate variable selection by domain ex- perts, that is, to systematically identify var- ious facets of a data set (each being based on a subset of attributes), cluster the data along each one, and present the results to the domain experts for appraisal and selection. In this paper, we propose a generalization of the Gaussian mixture model, show its abil- ity to cluster data along multiple facets, and demonstrate it is often more reasonable to fa- cilitate variable selection than to perform it.

Le California Verbal Learning Test (CVLT) est un instrument neuropsychologique issu des modèles de la psychologie cognitive qui produit un portrait dynamique de l'apprentissage verbal. Le but de la présente recherche est d'étudier la valeur psychométrique de l'adaptation française du CVLT. Les données recueillies auprès de 309 sujets normaux âgés de 17 à 90 ans, de 25 patients ayant subi un traumatisme cranio-encéphalique (TCE) et de 22 sujets bilingues ont permis d'apporter un soutien à cette forme du CVLT. La fidélité de la version française est démontrée à l'aide de trois analyses de consistance interne. Les indices de fidélité sont 0,93, 0,82 et 0,83. La validité de la version française est supportée par une analyse factorielle démontrant qu'il s'agit d'un outil qui couvre six construits théoriques de l'apprentissage, tel que souhaité lors de sa construction. La validité concomitante est appuyée par de fortes corrélations entre les résultats du CVLT français et ceux du WMS-R. Par ailleurs, les résultats démontrent également la capacité de cet instrument de détecter de façon significative les déficits de la mémoire chez une population de sujets TCE. Enfin, des corrélations significatives et une absence de différences sont obtenues entre les rendements donnés dans la majorité des variables des versions anglaise et française du CVLT. Toutefois, le nombre total de mots, le score de progression et le score de regroupements sémantiques soulignent des différences entre les deux formes. Il semble donc prématuré de conclure en leur équivalence. L'utilisation de l'adaptation du CVLT est néanmoins proposée aux chercheurs et aux cliniciens qui travaillent en langue française en raison du support psychométrique apporté par la présente étude et des biais possibles liés à la langue avec une simple traduction mot à mot de la version anglaise.

A fundamental problem in structural modeling and item response modeling is to determine whether a set items is unidimensional. Both models have a plethora of fit indicators, and the aim of this study is to compare the Stout T-statistic derived within item response modeling with many competing indices used in structural equation modeling to assess unidimensionality. Given that it is rare to find a “perfectly” unidimensional test, a further aim is to ascertain how many items from a second dimension are necessary before the various indices indicate that the item set is no longer “essentially” unidimensional.

A new ordination method, called co-correspondence analysis, is developed to relate two types of communities (e.g., a plant community and an animal community) sampled at a common set of sites in a direct way. The method improves the simple, indirect approach of applying correspondence analysis (reciprocal averaging) to the separate species data sets and correlating the resulting ordination axes. Co-correspondence analysis maxi- mizes the weighted covariance between weighted averaged species scores of one community and weighted averaged species scores of the other community. It thus attempts to identify the patterns that are common to both communities. Both a symmetric descriptive and an asymmetric predictive form are developed. The symmetric form relates to co-inertia analysis and the asymmetric, predictive form to partial least-squares regression. In two examples the predictive power of co-correspondence analysis is compared with that of canonical correspondence analyses on syntaxonomic and environmental data. In the first example, carabid beetles in roadside verges are shown to be more closely related to plant species composition than to vegetation structure (biomass, height, roughness, among others), and, in the second example, bryophytes in spring meadows are shown to be more closely related to the species composition of the vascular plants than to the measured water chemistry.

The behavior of the conditional logistic estimator is analyzed under a causal model for two-arm experimental studies with possible non-compliance in which the effect of the treatment is measured by a binary response variable. We show that, when non-compliance may only be observed in the treatment arm, the effect (measured on the logit scale) of the treatment on compliers and that of the control on non-compliers can be identified and consistently estimated under mild conditions. The same does not happen for the effect of the control on compliers. A simple correction of the conditional logistic estimator is then proposed, which allows us to considerably reduce the bias in estimating this quantity and the causal effect of the treatment over control on compliers. A two-step estimator results on the basis of which we can also set up a Wald test for the hypothesis of absence of a causal effect of the treatment. The asymptotic properties of the estimator are studied by exploiting the general theory on maximum likelihood estimation of misspecified models. Finite-sample properties of the estimator and of the related Wald test are studied by simulation. The extension of the approach to the case of missing responses is also outlined. The approach is illustrated by an application to a dataset deriving from a study on the efficacy of a training course on the breast self examination practice. Copyright (c) 2010 John Wiley & Sons, Ltd.

Designs that involve families (the traditional strength of genetic epidemiology) and population-based sampling (the traditional strength of environmental epidemiology) allow investigation of both genes and environment, separately or together, and allow valid inference to the population. These case-control-family designs (including those involving twin pairs), can be regarded as retrospective cohort studies of relatives, and can be used for: determining familial risks and genetic models; estimating risk (penetrance) for measured genotypes; genetic association studies; stratifying risks by family history and known mutation status; and studying modifiers of risk in genetically susceptible individuals. Follow-up of families allows genetic and environmental risks to be studied prospectively. We discuss statistical methods, theoretical and practical strengths, limitations, and other issues. Given their versatility, population-based family studies could become a principal framework in epidemiology, and move genetics from its traditional focus on high-risk families to give it a wider clinical and population health relevance.

Current genome-wide surveys of common diseases and complex traits fundamentally aim to detect indirect associations where the single nucleotide polymorphisms (SNPs) carrying the association signals are not biologically active but are in linkage disequilibrium (LD) with some unknown functional polymorphisms. Reproducing any novel discoveries from these genome-wide scans in independent studies is now a prerequisite for the putative findings to be accepted. Significant differences in patterns of LD between populations can affect the portability of phenotypic associations when the replication effort or meta-analyses are attempted in populations that are distinct from the original population in which the genome-wide study is performed. Here, we introduce a novel method for genome-wide analyses of LD variations between populations that allow the identification of candidate regions with different patterns of LD. The evidence of LD variation provided by the introduced method correlated with the degree of differences in the frequencies of the most common haplotype across the populations. Identified regions also resulted in greater variation in the success of replication attempts compared with random regions in the genome. A separate permutation strategy introduced for assessing LD variation in the absence of genome-wide data also correctly identified the expected variation in LD patterns in two well-established regions undergoing strong population-specific evolutionary pressure. Importantly, this method addresses whether a failure to reproduce a disease association in a disparate population is due to underlying differences in LD structure with an unknown functional polymorphism, which is vital in the current climate of replicating and fine-mapping established findings from genome-wide association studies.

Understanding the genetic architecture of quantitative traits begins with identifying the genes regulating these traits, mapping the subset of genetically varying quantitative trait loci (QTLs) in natural populations, and pinpointing the molecular polymorphisms defining QTL alleles. Studies in Drosophila have revealed large numbers of pleiotropic genes that interact epistatically to regulate quantitative traits, and large numbers of QTLs with sex-, environment- and genotype-specific effects. Multiple molecular polymorphisms in regulatory regions of candidate genes are often associated with variation for complex traits. These observations offer valuable lessons for understanding the genetic basis of variation for complex traits in other organisms, including humans.

BACKGROUND: Several techniques have been developed to detect differential item functioning (DIF), including ordinal logistic regression (OLR). This study compared different criteria for determining whether items have DIF using OLR. OBJECTIVES: To compare and contrast findings from three different sets of criteria for detecting DIF using OLR. General distress and physical functioning items were evaluated for DIF related to five covariates: age, marital status, gender, race, and Hispanic origin. RESEARCH DESIGN: Cross-sectional study. SUBJECTS: 1,714 patients with cancer or HIV/AIDS. MEASURES: A total of 23 items addressing physical functioning and 15 items addressing general distress were selected from a pool of 154 items from four different health-related quality of life questionnaires. RESULTS: The three sets of criteria produced qualitatively and quantitatively different results. Criteria based on statistical significance alone detected DIF in almost all the items, while alternative criteria based on magnitude detected DIF in far fewer items. Accounting for DIF by using demographic-group specific item parameters had negligible effects on individual scores, except for race. CONCLUSIONS: Specific criteria chosen to determine whether items have DIF have an impact on the findings. Criteria based entirely on statistical significance may detect small differences that are clinically negligible.

Clinimetrics is a methodologic discipline that focuses on the quality of clinical measurements, for example, diagnostic characteristics and disease outcomes. Different clinimetric properties, such as reproducibility and responsiveness, are important in both the development and the evaluation of measurement instruments. This article presents a number of the current challenges in clinimetrics: there is much confusion with regard to terminology, clinimetric properties are population and situation-dependent, and the abundance of different measurement instruments in specific fields hampers the comparison of study results. Further challenges lie in the improvement of the quality of both the measurement instruments and the performance of the actual measurements, and the assessment of the suitability for use in clinical practice. From the perspective of evidence-based medicine, it is essential to have measurement instruments that make it possible to detect clinically relevant improvements that are due to diagnostic and therapeutic interventions. Close collaboration between clinicians, statisticians, epidemiologists, and psychologists is necessary to guarantee healthy future developments in clinimetrics, serving the needs of both clinical research and clinical practice.

We introduce a novel latent grouping model for predicting the relevance of a new docu- ment to a user. The model assumes a la- tent group structure for both users and doc- uments. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimate both models by Gibbs sampling. The new method pre- dicts relevance more accurately for new doc- uments that have few known ratings. The reason is that generalization over documents then becomes necessary and hence the two- way grouping is profitable.

The question of what significance threshold is appropriate for genomewide association studies is somewhat unresolved. Previous theoretical suggestions have yet to be validated in practice, whereas permutation testing does not resolve a discrepancy between the genomewide multiplicity of the experiment and the subset of markers actually tested. We used genotypes from the Wellcome Trust Case-Control Consortium to estimate a genomewide significance threshold for the UK Caucasian population. We subsampled the genotypes at increasing densities, using permutation to estimate the nominal P-value for 5% family-wise error. By extrapolating to infinite density, we estimated the genomewide significance threshold to be about 7.2 x 10(-8). To reduce the computation time, we considered Patterson's eigenvalue estimator of the effective number of tests, but found it to be an order of magnitude too low for multiplicity correction. However, by fitting a Beta distribution to the minimum P-value from permutation replicates, we showed that the effective number is a useful heuristic and suggest that its estimation in this context is an open problem. We conclude that permutation is still needed to obtain genomewide significance thresholds, but with subsampling, extrapolation and estimation of an effective number of tests, the threshold can be standardized for all studies of the same population.

Human personality has been considered to have a neurochemical background. We examined the relation between extrastriatal dopamine D2 receptor binding in living human brain and the personality trait of novelty seeking that has been proposed to be related to dopaminergic function in the brain. We measured extrastriatal dopamine D2 receptors of 24 healthy young male subjects using [(11)C]FLB 457 positron emission tomography. The personality trait of each subject was assessed by the Temperament and Character Inventory (TCI). Correlation of dopamine D2 receptor binding with novelty seeking was calculated using region-of-interest analysis and statistical parametric mapping based on the binding potential images generated using a reference tissue model. A significant negative correlation was observed between binding potential values and the novelty seeking scores on TCI in the right insular cortex. No significant correlation was observed in any other region. Our result indicates that there is a significant association between dopamine D2 receptor binding and the human novelty seeking trait in the right insular cortex.

OBJECTIVE: Survival probability predictions in critically ill patients are mainly used to measure the efficacy of intensive care unit (ICU) treatment. The available models are functions induced from data on thousands of patients. Eventually, some of the variables used for these purposes are not part of the clinical routine, and may not be registered in some patients. In this paper, we propose a new method to build scoring functions able to make reliable predictions, though functions whose induction only requires records from a small set of patients described by a few variables. METHODS: We present a learning method based on the use of support vector machines (SVM), and a detailed study of its prediction performance, in different contexts, of groups of variables defined according to the source of information: monitoring devices, laboratory findings, and demographic and diagnostic features. RESULTS: We employed a data set collected in general ICUs at 10 units of hospitals in Spain, 6 of which include coronary patients, while the other 4 do not treat coronary diseases. The total number of patients considered in our study was 2501, 19.83% of whom did not survive. Using these data, we report a comparison between the SVM method proposed here with other approaches based on logistic regression (LR), including a second-level recalibration of release III of the acute physiology and chronic health evaluation (APACHE, a scoring system commonly used in ICUs) induced from the available data. The SVM method significantly outperforms them all from a statistical point of view. Comparison with the commercial version of APACHE III shows that the SVM scores are slightly better when working with data sets of more than 500 patients. CONCLUSIONS: From a practical point of view, the implications of the research reported here may be helpful to address the construction of cheap and reliable prediction systems in accordance with the peculiarities of ICUs and kinds of patients.

Complex diseases are presumed to be the results of interactions of several genes and environmental factors, with each gene only having a small effect on the disease. Thus, the methods that can account for gene-gene interactions to search for a set of marker loci in different genes or across genome and to analyze these loci jointly are critical. In this article, we propose an ensemble learning approach (ELA) to detect a set of loci whose main and interaction effects jointly have a significant association with the trait. In the ELA, we first search for "base learners" and then combine the effects of the base learners by a linear model. Each base learner represents a main effect or an interaction effect. The result of the ELA is easy to interpret. When the ELA is applied to analyze a data set, we can get a final model, an overall P-value of the association test between the set of loci involved in the final model and the trait, and an importance measure for each base learner and each marker involved in the final model. The final model is a linear combination of some base learners. We know which base learner represents a main effect and which one represents an interaction effect. The importance measure of each base learner or marker can tell us the relative importance of the base learner or marker in the final model. We used intensive simulation studies as well as a real data set to evaluate the performance of the ELA. Our simulation studies demonstrated that the ELA is more powerful than the single-marker test in all the simulation scenarios. The ELA also outperformed the other three existing multi-locus methods in almost all cases. In an application to a large-scale case-control study for Type 2 diabetes, the ELA identified 11 single nucleotide polymorphisms that have a significant multi-locus effect (P-value=0.01), while none of the single nucleotide polymorphisms showed significant marginal effects and none of the two-locus combinations showed significant two-locus interaction effects.

Principal coordinate analysis (PCO), a dual of principal component analysis (PCA), is a classical method for exploratory data analysis. In this paper we provide a probabilistic interpretation of PCO. We show that this interpretation yields a maximum likelihood procedure for estimating the PCO parameters and we also present an iterative expectation-maximization algorithm for obtaining maximum likelihood estimates. Finally, we show that our framework yields a probabilistic formulation of kernel PCA.

OBJECTIVES: To assess biopsychosocial predictors of health-related quality of life (HRQOL) in patients with chronic hepatitis C. METHODS: In 94 consecutive patients with chronic hepatitis C attending a liver center, HRQOL was assessed by the Medical Outcome Study Short Form Health Survey 36 (SF-36) and by the German version of the Chronic Liver Disease Questionnaire. The predictive effect on HRQOL of disease-related worries measured by the worry subscale of the Chronic Liver Disease Questionnaire, psychiatric comorbidity (defined by at least one Hospital Anxiety and Depression Scale German Version Score > or =11), the Child-Pugh score in case of cirrhosis, interferon therapy, and active medical comorbidities was assessed by a multiple regression analysis. RESULTS: From 88 patients (age, 48.6 +/- 14.6 years; 50% female), 62 (70%) had no cirrhosis, 15 (17%) Child A, 5 (6%) Child B, and 6 patients (7%) Child C cirrhosis. The mental summary score of SF-36 was predicted by the amount of disease-related worries (corrected R2 = 0.33; beta = 3.2; p < .001) and psychiatric comorbidity (corrected R2 = 0.42; beta = -9.0; p < .001), by the physical summary score of SF-36 by the amount of disease related worries (corrected R2 = 0.33; beta = 4.0; p < .001), and by the number of active medical comorbidities (corrected R2 = 0.39; beta = -2.0; p = .006). CONCLUSIONS: The HRQOL in chronic hepatitis C is not determined by the severity of the liver disease but by psychiatric and medical comorbidities and disease-related worries.

The process of making judgments and decisions requires a method for combining data. To compare the accuracy of clinical and mechanical (formal, statistical) data-combination techniques, we performed a meta-analysis on studies of human health and behavior. On average, mechanical-prediction techniques were about 10% more accurate than clinical predictions. Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33%-47% of studies examined. Although clinical predictions were often as accurate as mechanical predictions, in only a few studies (6%-16%) were they substantially more accurate. Superiority for mechanical-prediction techniques was consistent, regardless of the judgment task, type of judges, judges' amounts of experience, or the types of data being combined. Clinical predictions performed relatively less well when predictors included clinical interview data. These data indicate that mechanical predictions of human behaviors are equal or superior to clinical prediction methods for a wide range of circumstances.

In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal and time-to-event data.

This article reviews six dimension-specific health-related quality of life (HRQL) measures which have been used cross-culturally. The instruments reviewed are: the Beck Depression Inventory (BDI); the McGill Pain Questionnaire (MPQ); the Center for Epidemiologic Studies-Depression (CES-D); the Zung Self-Rating Depression Scale (SDS); the General Health Questionnaire (GHQ); and the Psychological General Well-Being Index (PGWB). These instruments primarily represent the psychological or emotional dimension of HRQL, and are scales that were developed and validated in the USA, Canada or the UK. The review of specific studies for each of the six instruments was not meant to be exhaustive, but rather to give an indication of the ways in which the instruments have been assessed or used in various countries. The focus throughout this article is on the psychometric properties (reliability, validity and responsiveness) of these scales in different cultures, as well as the processes used to translate the instruments from English into another language. Implications of the results of this review for cross-cultural use of dimension-specific HRQL instruments are drawn.

Summary. The reliability of multi-item scales has received a lot of attention in the psychometric literature, where a myriad of measures like the Cronbach's alpha or the Spearman-Brown formula have been proposed. Most of these measures, however, are based on very restrictive models that apply only to unidimensional instruments. In this article, we introduce two measures to quantify the reliability of multi-item scales based on a more general model. We show that they capture two different aspects of the reliability problem and satisfy a minimum set of intuitive properties. The relevance and complementary value of the measures is studied and earlier approaches are placed in a broader theoretical framework. Finally, we apply them to investigate the reliability of the Positive and Negative Syndrome Scale, a rating scale for the assessment of the severity of schizophrenia.

A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case control data and yet, like family-based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.

BACKGROUND: Heavy alcohol use, hepatitis C and illicit drug use each have been shown to have negative impacts on health-related quality of life (HRQL). To date, considerations of HRQL have not played a prominent role in the design and measurement of intervention strategies for out-of-treatment at-risk populations. METHODS: Data were collected from out-of-treatment IDUs recruited through street outreach in North Carolina. Multiple linear regression analyses were used to examine the independent effects of HCV status, harmful drinking (AUDIT), and illicit drug use on HRQL (SF-36). RESULTS: Fifty-one percent of 619 study participants tested HCV-positive; 57% met criteria for harmful or hazardous drinking and 63% reported daily use of hard drugs. HRQL scores for this population were significantly lower than those of the general population. Multiple linear regression analyses demonstrated that harmful levels of alcohol consumption and use of methamphetamine in the past month had the strongest associations with reduced HRQL. CONCLUSIONS: Given the high rates of HCV in most IDU communities, new harm reduction approaches are needed for these populations which focus beyond prevention to the functioning and well being of those already infected. In particular, reducing heavy alcohol use in addition to slowing HCV progression shows promise for improving HRQL.

BACKGROUND: We studied the hypothesis of socioeconomic equalization regarding adolescents' mental health problems by examining whether a low educational level of adolescents and their parents shows independent (cumulative) or dependent (including interactive) associations with adolescents' mental health problems, or whether equalization occurred. METHODS: Cross-sectional data were obtained from the preventive Youth Health Care Centre in a relatively deprived Dutch former mining area. Participants were 1861 adolescents aged 13 or 14 years (response rate 71.7%). The self-administered Dutch version of the Strengths and Difficulties Questionnaire (SDQ) was used to identify adolescents' mental health problems. Multiple logistic regression analyses were used to examine the associations, and linear regression models to check the robustness of the findings. RESULTS: A low educational level of adolescents was strongly related to their mental health problems (OR = 5.37; 95% CI: 3.31-8.70). The initially high odds ratios for adolescents with low-educated parents (OR = 1.72; 95% CI: 1.14-2.59) disappeared after controlling for the adolescents' own educational level (OR = 1.12; 95% CI: 0.73-1.74). In terms of interactions, no specifically increased odds were found, e.g. for low-educated adolescents with high-educated parents. CONCLUSION: There was no evidence for socioeconomic equalization regarding adolescents' mental health problems. Lower educated adolescents had substantially higher odds of having mental health problems, regardless of their parents' education. The odds may be affected by differences in intelligence and life events. Youth healthcare workers should collaborate closely with schools to intervene in time, particularly among low-educated adolescents. More interventions are probably needed to reduce these major inequities.

Adolescence is usually defined as the period of psychological and social transition between childhood and adulthood. The beginning of adolescence, around the onset of puberty, is characterized by large hormonal and physical changes. The transition from childhood to adulthood is also characterized by psychological changes in terms of identity, self-consciousness, and cognitive flexibility. In the past decade, it has been demonstrated that various regions of the human brain undergo development during adolescence and beyond. Some of the brain regions that undergo particularly protracted development are involved in social cognitive function in adults. In the first section of this paper, I briefly describe evidence for a circumscribed network of brain regions involved in understanding other people. Next, I describe evidence that some of these brain regions undergo structural development during adolescence. Finally, I discuss recent studies that have investigated social cognitive development during adolescence.

A reward or punishment can seem better or worse depending on what else might have happened. Little is known, however, about how neural representations of an anticipated incentive might be influenced by the available alternatives. We used event-related FMRI to investigate the activation in the nucleus accumbens (NAcc), while we varied the available alternative incentives in a monetary incentive delay task. Some task blocks included only uncertain gains and losses; others included the same uncertain gains and losses intermixed with certain gains and losses. The availability of certain gains and losses increased NAcc activation for uncertain losses and decreased the difference between uncertain gains and losses. We suggest that this pattern of activation can result from reference point changes across blocks, and that the worst available loss may serve as an important anchor for NAcc activation. These findings imply that NAcc activation represents anticipated incentive value relative to the current context of available alternative gains and losses.

Copy number variations (CNVs) are universal genetic variations, and their association with disease has been increasingly recognized. We designed high-density microarrays for CNVs, and detected 3000-4000 CNVs (4-6% of the genomic sequence) per population that included CNVs previously missed because of smaller sizes and residing in segmental duplications. The patterns of CNVs across individuals were surprisingly simple at the kilo-base scale, suggesting the applicability of a simple genetic analysis for these genetic loci. We utilized the probabilistic theory to determine integer copy numbers of CNVs and employed a recently developed phasing tool to estimate the population frequencies of integer copy number alleles and CNV-SNP haplotypes. The results showed a tendency toward a lower frequency of CNV alleles and that most of our CNVs were explained only by zero-, one- and two-copy alleles. Using the estimated population frequencies, we found several CNV regions with exceptionally high population differentiation. Investigation of CNV-SNP linkage disequilibrium (LD) for 500-900 bi- and multi-allelic CNVs per population revealed that previous conflicting reports on bi-allelic LD were unexpectedly consistent and explained by an LD increase correlated with deletion-allele frequencies. Typically, the bi-allelic LD was lower than SNP-SNP LD, whereas the multi-allelic LD was somewhat stronger than the bi-allelic LD. After further investigation of tag SNPs for CNVs, we conclude that the customary tagging strategy for disease association studies can be applicable for common deletion CNVs, but direct interrogation is needed for other types of CNVs.

Cet article est une application très simple de recherches développées par Tallis et Scheinberg. Il rappelle l'expression de la variance d'échantillonnage de l'estimation de coefficients de corré- lation (génétique,génotypique,dueàl'environnement,phénotypique)obtenusen analysede variance- covariance. Il particularise au cas d'un test clonal et montre que l'on commet une erreur parfois importante en jugeant la signification des corrélations génotypique et phénotypique uniquement à l'aide des caracté- ristiques du dispositif : nombre de clones, nombre d'individus par clone.

OBJECTIVE: The Neonatal Individualized Developmental Care Program (NIDCAP) for very low birth weight (VLBW) preterm infants has been suggested by Als et al to improve several medical outcome variables such as time on ventilator, time to nipple feed, the duration of hospital stay, better behavioral performance on Assessment of Preterm Infants' Behavior (APIB), and improved neurodevelopmental outcomes. We have tested the hypothesis of whether the infants who had received NIDCAP would show advanced sleep-wake pattern, behavioral, and neurodevelopmental outcome. METHODS: Thirty-five VLBW infants were randomly assigned to receive NIDCAP or routine infant care. The goals for NIDCAP intervention were to enhance comfort and stability and to reduce stress and agitation for the preterm infants by: a) altering the environment by decreasing excess light and noise in the neonatal intensive care unit (NICU) and by using covers over the incubators and cribs; b) use of positioning aids such as boundary supports, nests, and buntings to promote a balance of flexion and extension postures; c) modification of direct hands-on caregiving to maximize preparation of infants for, tolerance of, and facilitation of recovery from interventions; d) promotion of self-regulatory behaviors such as holding on, grasping, and sucking; e) attention to the readiness for and the ability to take oral feedings; and f) involving parents in the care of their infants as much as possible. The infants' sleep was recorded at 36 weeks postconceptional age (PCA) and at 3 months corrected age (CA) using the Motility Monitoring System (MMS), an automated, nonintrusive procedure for determining sleep state from movement and respiration patterns. Behavioral and developmental outcome was assessed by the Neurobehavioral Assessment of the Preterm Infant (NAPI) at 36 weeks PCA, the APIB at 42 weeks PCA, and by the Bayley Scales of Infant Development (BSID) at 4, 12, and 24 months CA. RESULTS: Sleep developmental measures at 3 months CA showed a clear developmental change compared with 36 weeks PCA. These include: increased amount of quiet sleep, reduced active sleep and indeterminate sleep, decreased arousal, and transitions during sleep. Longest sleep period at night showed a clear developmental effect (increased) when comparing nighttime sleep pattern of infants at 3 months with those at 36 weeks of age. Day-night rhythm of sleep-wake increased significantly from 36 weeks PCA to 3 months CA. However, neither of these sleep developmental changes showed any significant effects of NIDCAP intervention. Although all APIB measures showed better organized behavior in NIDCAP patients, neither NAPI nor Bayley showed any developmental advantages for the intervention group. The neurodevelopmental outcome measured by the Bayley at 4, 12, and 24 months CA showed 64% of the NIDCAP intervention group at the lowest possible score compared with 33% of the control group. These findings could not be explained by the occurrence of intraventricular hemorrhage or the socioeconomic status of the parents, which showed no significant group effect. CONCLUSION: The results of this study, including measures of sleep maturation and neurodevelopmental outcome up to 2 years of age did not demonstrate that the NIDCAP intervention results in increased maturity or development. Buehler et al (Pediatrics. 1995;96:923-932) have reported that premature infants (N = 12; mean gestational age 32 weeks, mean birth weight 1700 g) who received developmental care compared with a similar group of infants who received routine care showed better organized behavioral performance on an APIB assessment at 42 weeks PCA. None of the medical outcome measures were significantly different in this study. Although our APIB results are in agreement, the results of the NAPI, the Bayley and sleep measures do not show an increase in neurodevelopmental maturation. In the earlier report by Als et al (Journal of the American Medical Associatio

Fundamental biological knowledge and the technology to acquire it have been immeasurably advanced by past efforts to understand and manipulate the genomes of model organisms. Has the utility of bacteria, yeast, worms, flies, mice, plants, and other models now peaked and are humans poised to become the model organism of the future? The Genetics Society of America recently convened its 2006 meeting entitled "Genetic Analysis: Model Organisms to Human Biology" to examine the future role of genetic research. (Because of time limitations, the meeting was unable to cover the substantial contributions and future potential of research on model prokaryotic organisms.) In fact, the potential of model-organism-based studies has grown substantially in recent years. The genomics revolution has revealed an underlying unity between the cells and tissues of eukaryotic organisms from yeast to humans. No uniquely human biological mechanisms have yet come to light. This common evolutionary heritage makes it possible to use genetically tractable organisms to model important aspects of human medical disorders such as cancer, birth defects, neurological dysfunction, reproductive failure, malnutrition, and aging in systems amenable to rapid and powerful experimentation. Applying model systems in this way will allow us to identify common genes, proteins, and processes that underlie human medical conditions. It will allow us to systematically decipher the gene-gene and gene-environment interactions that influence complex multigenic disorders. Above all, disease models have the potential to address a growing gap between our ability to collect human genetic data and to productively interpret and apply it. If model organism research is supported with these goals in mind, we can look forward to diagnosing and treating human disease using information from multiple systems and to a medical science built on the unified history of life on earth.

The assessment of handedness is of interest in some psychiatric populations, above all in schizophrenic patients, because there may be a relationship between neurodevelopmental, hemispheric damage and psychiatric disease processes (Crow TJ. Schizophrenia Bulletin 1990;16:433-443; Tyler M, Diamond J, Lewis S. Schizophrenia Research 1995;18:37-41). Various methods to assess handedness have been proposed. In order to detect the most precise instrument for the assessment of handedness, two different measures, a questionnaire and a computational procedure for movement analysis, were compared in a group of healthy subjects. The ability of the methods to discriminate not only between the groups of right-handers (n=12) and left-handers (n=23), but also between left-handers trained in school to use the non-dominant right hand ('inconsistent' left-handers; n=11) and those allowed to use their left hand for writing ('consistent' left-handers; n=12) was investigated. For future investigations, our main concern was to determine if one method had superiority over the other. The results revealed that the Edinburgh Handedness Inventory (EHI) distinguishes just as well as the computational method between right-handers and non-right-handers. However, more precise discrimination between the subgroups of 'consistent' and 'inconsistent' left-handers is possible using digitized analysis of hand-motor performance. According to our results handedness should be assessed not only with the EHI, but also with the computer-aided analysis of hand-movements.

OBJECTIVE: To explore whether it is possible to construct clinical measures of functioning by integrating information obtained across the categories of the International Classification of Functioning, Disability, and Health (ICF) using the ICF Core Set of osteoarthritis (OA) as a case in point. STUDY DESIGN AND SETTING: Psychometric study using data from 437 patients with OA from Germany, Italy, Hungary, Serbia, and Singapore. The analyses were performed with the ICF categories of the comprehensive ICF Core Set for OA addressing functioning and using the Rasch model for ordered response options. RESULTS: A clinical measure with 74 country-specific and seven common ICF categories was created with the pooled data of all countries but Hungary. The overall fit statistic according to the chi(2) was chi(df=405)(2)=451.73, P=0.054, and the Z-fit statistic was Z(mean)=-0.041 (Z(standard deviation [SD])=1.01) for items and Z(mean)=-0.15 (Z(SD)=1.19) for persons. The Person Separation Index r(beta) was 0.92. CONCLUSION: For the first time, a cross-cultural clinical measure of functioning was constructed which integrates ICF categories. The results of this investigation are promising and can contribute to the acceptance and usefulness of the ICF in clinical practice.

BACKGROUND: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. RESULTS: Random forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. CONCLUSION: Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.

Researchers are increasingly interested in studying the effects of the social environment on health.1 The concept of social capital has been put forward as one explanation for why some communities work better than others, with benefits for the whole of the local population.2 Social capital is applied to those features of a community that promote cohesion and a sense of belonging and that enable its members to cooperate. Similarly, criminologists have argued that the level of social organisation in a neighbourhood, or the degree to which residents are able to realise common goals and exercise social control, links the social composition of a neighbourhood and rates of deviant behaviour.3 We investigated how individual's reports of social capital and social disorganisation are associated with health outcomes among men and women aged 16 to 54 from a representative cross section of British households.

A distributed cognition perspective on teacher evaluation demonstrates how context and cognition interact to form systems of knowing and action. In this paper, we discuss how a middle school principal used a standards-based teacher evaluation system with her teachers. The task of teacher evaluation was distributed through several artifacts designed to structure the evaluation process. The central, district-designed artifact included features to support both formative and summative aspects of evaluation. In practice, the principal as evaluator used her discretion to determine which features of the program to implement when. Implementing the evaluation system required the principal to assess the place of evaluation among the existing features of the local school system to develop a process that made teacher evaluation a tool for meaningful communication about instruction, improvement, and status within the school.

The authors examined the developmental course of self-regulation in a cohort of children from the National Longitudinal Survey of Youth. The longitudinal sample included 646 children (48% girls; 52% boys; 36.2% Black, 23.4% Hispanic, 40.4% White) who were 4 to 5 years old in 1986 and who were followed up at ages 8 to 9 and ages 12 to 13. Levels of self-regulation (assessed with 12 maternal-report items that measured regulation of affect, behavior, attention) increased from early childhood (when sample children were 4 or 5 years old) to middle childhood (ages 8 or 9), but not from middle childhood to early adolescence (ages 12 or 13). Girls exhibited significantly higher levels of self-regulation than did boys at all 3 time points. Individual differences in self-regulation were fairly stable across the 8-year span (rs = .47 to .50). Comparisons of 1-, 2-, and 3-factor models suggested that the different aspects of self-regulation are highly interrelated, and support adoption of a single-factor model for both genders. The authors discuss implications of these findings for theory and intervention.

Nonsymmetric correspondence analysis (NSCA) is designed to analyze two-way con- tingency tables in which rows and columns assume an asymmetric role, e.g., columns depend on rows, but not vice versa. A ridge type of regularization was incorporated into a variety of NSCA: Ordinary NSCA, and Partial and/or Constrained NSCA. The regularization has proven useful in obtaining estimates of parameters, which are on average closer to the true population values. An optimal value of the reg- ularization parameter is found by a G-fold cross validation method, and the best dimensionality of the solution space is determined by permutation tests. A bootstrap method is used to evaluate the stability of the solution. A small Monte Carlo study and an illustrative example demonstrate the usefulness of the proposed procedures.

Impulsivity, a highly prevalent symptom in multiple psychiatric disorders, is a partially heritable trait influenced by specific biological mechanisms. In particular, dopamine is proposed to play a role in impulsive behaviors and recent studies have implicated functional polymorphisms of dopamine-related genes in impulsive behaviors across different clinical and behavioral classifications. However, most have not isolated the impulsivity construct per se as a biologically based and measurable endophenotype. The present study was therefore undertaken in a sample of healthy adults to investigate the influence of two candidate dopaminergic gene polymorphisms (DRD4 and DAT) on the endophenotype of impulsivity, which we operationalized as behavioral inhibition during the Stop-signal task. We recruited an ethnically diverse sample of 119 healthy adults to complete a self-report questionnaire of impulsivity and to perform a Stop-signal task. We report significant differences in inhibitory control between individuals with at least one 7-repeat allele of the DRD4 polymorphism, as well as an interaction between DRD4 and DAT genotypes, on inhibitory control. Results of the present study support the influence of dopaminergic variation on impulsive-related measures, as well as the advantage of using measures which are likely more sensitive to the effects of such genetic variation.

OBJECTIVE: To evaluate the validity of the EuroQol (EQ-5D) in a population of chronic, treatment-resistant heroin-dependent patients. METHODS: The EQ-5D is studied relative to the Maudsley Addiction Profile (MAP), the Symptom Checklist (SCL-90) and the European Addiction Severity Index (EuropASI) which were used to assess the participant's physical functioning, mental health and social integration, respectively. Data were gathered from 430 patients participating in the Dutch heroin trials with an intended 12-month treatment period. The EQ-5D was used as a separate health outcome measure. Statistical analyses were conducted using Spearman's and Pearson's correlations. RESULTS: The EQ-5D dimensions mobility, self-care and usual activities generally showed low correlations with relevant parameters of the MAP-HSS, SCL-90 and EuropASI (r=0.132-0.369). The EQ-5D dimension pain/discomfort showed low to moderate hypothesized correlations with all disease-specific measures (r=0.153-0.496). The EQ-5D dimension anxiety/depression showed moderate to high correlations with the SCL-90 (including the sum score) and some of the EuropASI parameters (r=0.133-0.615). The EQ-5D utility scores were moderately correlated with the MAP-HSS (r=-0.468) and the SCL-90 (r=-0.491) total score and with response to treatment at month 12. CONCLUSION: The majority of hypothesized associations between the EQ-5D and the disease or domain-specific measures could be confirmed. The validity of the EQ-5D-based utility score appears to be suitable in the evaluation of chronic, heroin-dependent populations.

The article uses confirmatory factor analysis (CFA) as a template to explain didactically multilevel structural equation models (ML-SEM) and to demonstrate the equivalence of general mixed-effects models and ML-SEM. An intuitively appealing graphical representa- tion of complex ML-SEMs is introduced that succinctly describes the underlying model and its assumptions. The use of definition variables (i.e., observed variables used to fix model parameters to individual specific data values) is extended to the case of ML-SEMs for clustered data with random slopes. Empirical examples of multilevel CFA and ML-SEM with random slopes are provided along with scripts for fitting such models in SAS Proc Mixed, Mplus, and Mx. Methodological issues regarding estimation of complex ML-SEMs and the evaluation of model fit are discussed. Further potential applications of ML-SEMs are explored.

This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic prob- lems and that this literature can be considerably improved by greater attention to these issues.

Objective: This paper compares four techniques used to assess change in neuropsychological test scores before and after coronary artery bypass graft surgery (CABG), and includes a rationale for the classification of a patient as overall impaired. Methods: A total of 55 patients were tested before and after surgery on the MicroCog neuropsychological test battery. A matched control group underwent the same testing regime to generate test-retest reliabilities and practice effects. Two techniques designed to assess statistical change were used: the Reliable Change Index (RCI), modified for practice, and the Standardised Regression-based (SRB) technique. These were compared against two fixed cutoff techniques (standard deviation and 20% change methods). Results: The incidence of decline across test scores varied markedly depending on which technique was used to describe change. The SRB method identified more patients as declined on most measures. In comparison, the two fixed cutoff techniques displayed relatively reduced sensitivity in the detection of change. Conclusions: Overall change in an individual can be described provided the investigators choose a rational cutoff based on likely spread of scores due to chance. A cutoff value of 20% of test scores used provided acceptable probability based on the number of tests commonly encountered. Investigators must also choose a test battery that minimises shared variance among test scores.

BACKGROUND: Understanding variations in the incidence of schizophrenia is a crucial step in unravelling the aetiology of this group of disorders. The aims of this review are to systematically identify studies related to the incidence of schizophrenia, to describe the key features of these studies, and to explore the distribution of rates derived from these studies. METHODS: Studies with original data related to the incidence of schizophrenia (published 1965-2001) were identified via searching electronic databases, reviewing citations and writing to authors. These studies were divided into core studies, migrant studies, cohort studies and studies based on Other Special Groups. Between- and within-study filters were applied in order to identify discrete rates. Cumulative plots of these rates were made and these distributions were compared when the underlying rates were sorted according to sex, urbanicity, migrant status and various methodological features. RESULTS: We identified 100 core studies, 24 migrant studies, 23 cohort studies and 14 studies based on Other Special Groups. These studies, which were drawn from 33 countries, generated a total of 1,458 rates. Based on discrete core data for persons (55 studies and 170 rates), the distribution of rates was asymmetric and had a median value (10%-90% quantile) of 15.2 (7.7-43.0) per 100,000. The distribution of rates was significantly higher in males compared to females; the male/female rate ratio median (10%-90% quantile) was 1.40 (0.9-2.4). Those studies conducted in urban versus mixed urban-rural catchment areas generated significantly higher rate distributions. The distribution of rates in migrants was significantly higher compared to native-born; the migrant/native-born rate ratio median (10%-90% quantile) was 4.6 (1.0-12.8). Apart from the finding that older studies reported higher rates, other study features were not associated with significantly different rate distributions (e.g. overall quality, methods related to case finding, diagnostic confirmation and criteria, the use of age-standardization and age range). CONCLUSIONS: There is a wealth of data available on the incidence of schizophrenia. The width and skew of the rate distribution, and the significant impact of sex, urbanicity and migrant status on these distributions, indicate substantial variations in the incidence of schizophrenia. Mesh-terms: Cohort Studies; Female; Humans; Incidence; Male; Rural Health; Schizophrenia :: epidemiology; Sex Distribution; Transients and Migrants :: statistics & numerical data; Urban Health;

Facial expression of emotion is a key mechanism of non-verbal social communication in humans. Deficits in processing of facial emotion have been implicated in psychiatric disorders characterized by abnormal social behavior, such as autism and schizophrenia. Identification of genetically transmitted variability in the neural substrates of facial processing can elucidate the pathways mediating genetic influences on social behavior and provide useful endophenotypes for psychiatric genetic research. This study examined event-related brain potentials (ERPs) evoked by changes in facial expression in adolescent twins (age 12, 47 monozygotic and 51 dizygotic pairs). Facial images with happy, fearful, and neutral expressions were administered in a continuous mode, such that different expressions of the same face instantaneously replaced each other. This experimental design allowed us to isolate responses elicited by changes in emotional expression that were not confounded with responses elicited by image onset. Changes of emotional expression elicited a N240 wave with a right temporoparietal maximum and a P300 wave with a centropariatal midline maximum. Genetic analyses using a model fitting approach showed that a substantial proportion of the observed individual variation in these ERP responses can be attributed to genetic factors (36-64% for N250 and 42-62% for P300 components, respectively). This study provides the first evidence for heritability of neuroelectric indicators of face processing and suggests that ERP components sensitive to emotional expressions can potentially serve as endophenotypes for psychpathology characterized by abnormalities in social cognition and behavior.

This study explored the relationships among children's ability to control impulsive behaviors, academic motivation, and academic performance. Results showed that, as early as when children are in kindergarten and first grade, their ability to control impulsiveness and their academic motivation both positively influence academic performance. However, academic motivation does not mediate between children's ability to control impulsiveness and their academic performance. Different aspects of children's self-concepts (social competence and academic competence) are closely associated with each other, but academic motivation does not influence that association. Data were extracted from a national database-the Early Childhood Longitudinal Study- Kindergarten Class of 1998-99.

Numerous genetic association studies for complex diseases are performed. Investigators place emphasis on formal statistical significance (P-values < 0.05), but the predictive ability of early statistically significant ('positive') findings is unclear. We scrutinized 55 cumulative meta-analyses of genetic associations (579 studies), in order to assess whether having statistical significance in the earliest (first) published study or in at least half among several (> or =3) early-published studies, or high statistical significance in early studies had any predictive ability for establishing or refuting the presence of the genetic association in subsequent research. In 35 associations, a first study was 'positive' and in 15 associations more than half of the early-published reports were 'positive'. The average publication rate of subsequent studies increased 1.71-fold with a 'positive' first report. When compared against the summary results of subsequent research, sensitivity and specificity were 0.65 and 0.38 for the first reports, and 0.40 and 0.73, respectively, when at least three early studies were considered. First studies also had poor predictive ability, when we considered the estimated attributable fraction and coverage of the 95% confidence interval thereof or higher levels of statistical significance. We conclude that although 'positive' findings in the very first reports provide strong incentive for conducting more studies on a putative genetic epidemiological association, the statistical significance or even the magnitude of the effect of early studies cannot adequately predict eventual establishment of an association. Conversely, many genuine epidemiological associations would be missed, if research were abandoned after early underpowered 'negative' studies.

Hotspots of meiotic recombination can change rapidly over time. This instability and the reported high level of inter-individual variation in meiotic recombination puts in question the accuracy of the calculated hotspot map, which is based on the summation of past genetic crossovers. To estimate the accuracy of the computed recombination rate map, we have mapped genetic crossovers to a median resolution of 70 Kb in 10 CEPH pedigrees. We then compared the positions of crossovers with the hotspots computed from HapMap data and performed extensive computer simulations to compare the observed distributions of crossovers with the distributions expected from the calculated recombination rate maps. Here we show that a population-averaged hotspot map computed from linkage disequilibrium data predicts well present-day genetic crossovers. We find that computed hotspot maps accurately estimate both the strength and the position of meiotic hotspots. An in-depth examination of not-predicted crossovers shows that they are preferentially located in regions where hotspots are found in other populations. In summary, we find that by combining several computed population-specific maps we can capture the variation in individual hotspots to generate a hotspot map that can predict almost all present-day genetic crossovers.

To what extent do legislative parties shape legislative outcomes? There are strong theoretical reasons to believe that legislative institutions, in particular political parties, have a powerful influence on legislative outcomes by shaping how legislators' personal preferences over policies translate into “revealed” legislative behavior and hence ultimately policy outcomes. Yet, estimating the impact of legislative parties on legislative behaviour is often difficult: cohesive voting among party members could indicate that the party has enforced disciplined voting, or that members share the same preferences on the issue of the vote. The relevant coun- terfactual is how MEPs would have voted in the absense of European or national party influence. We examine these issues in the context of the European Parliament, modeling roll-call votes in a Bayesian hierarchical framework as a function of both party and preferences. If answers to a European Policy Research Group survey are taken as an exogenous mea- sure of MEPs' preferences, they indicate wide intra-party variation and considerable inter-party overlap. But, as other research has shown, summaries of their voting behavior show them to be neatly divided by party group. We develop an extension of the item-response model in which the latent ideal point is a function of a set of predictors including party group, nationality, and surveyed preference. In other words, rather than attempting to explain variation among summaries of vote behavior, we estimate the effects of party and preference directly on MEPs' complete vote histories. Finally, we are able to formally compare models with arbitrary predictors - including national party, european party, nationality, or alternative measures of preference - via the marginal likelihood.