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\useURL[article-src][http://www.aliquote.org/memos/2008/04/15/penalized-likelihood-regression][][aliquote.org/memos/2008/04/15/penalized-likelihood-regression]
\useURL[article-cit][http://www.unc.edu/~enorton/AiNorton.pdf][][pdf version]

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\midaligned{\bfb Practical Psychiatric Epidemiology -- Statistics in Psychiatry}
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\startadmonition
This article was first published on {\tt www.aliquote.org} (April, 2008).
\stopadmonition
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\useURL[medstats][http://groups.google.com/group/MedStats][][Medstats]
\useURL[review][http://bjp.rcpsych.org/cgi/content/full/186/3/268-a][][review]
\useURL[epidemiology][http://en.wikipedia.org/wiki/Epidemiology][][epidemiology]

Here is a short review of
{\em Practical Psychiatric Epidemiology}, Prince, M., Stewart, R., Ford, T., and
Hotopf, M. (Eds.) (Oxford, 2003), and of {\em Statistics in Psychiatry},
G. Dunn (Arnold, 2000). 

\section{Practical Psychiatric Epidemiology}
There is already a \from[review] in {\em The Bristish Journal of Psychiatry}
(2005, 186: 268). Hereafter, I would like to quote some of the main ideas of
this nice textbook on research and methodological aspects of psychiatric
epidemiology. Although this textbook is mostly dedicated to students in
epidemiology or psychiatry (which I am not, of course), it contains a huge
amount of useful references and advices to whom may be concerned with the
analysis of comparative studies in the biomedical domain. The growing interest
in genetic-related studies and the now available statistical methods to deal
with large-scale surveys or Quality of Life studies allow for a deeper
understanding and takings of mental health problems. These emerging issues in
psychiatric epidemiology offer a new research area open to inter-disciplinary
collaboration. 

Basically, psychiatric epidemiology is concerned with the study of prognosis
and treatment of psychiatric disorders. Contrary to more \quote{classical}
\from[epidemiology], study on mental health related pathology does not always
involve biological events or signals, thus emphasizing the need to build
questionnaire or dedicated interview that might lead to some useable manifest
variables. With this in mind, some of this work has more to do with usual
psychometrics (i.e. the science of the measurement of mental
phenomena). Generally speaking, work related to health care aims at answering
three kind of question:
\startitemize
\item {\em What is going on?} Only history taking, examination and diagnosis
  allow the practician to deal with such a question. 
\item {\em Why?} Answer to this question may be provided by aetiological
  research based on transversal or longitudinal studies. 
\item {\em What do we do about it?} Answer to this question relies on
  intervention studies, including RCTs, but is obviously associated with some
  ethical problems.
\stopitemize

These topics are treated at length in Section 2 of the textbook, which we will
resume in a few paragraphs on. Further, quoting Eaton (1986) cited on page 5,
\startquotation
Epidemiology is a branch of medicine, and thus the assumptions of the medical
model of disease are implicit. The most important assumption is that the
disease under study actually exists\dots In psychiatry this assumption is
assuredly more tenuous than in other areas of medicine, because psychiatric
diseases tend to be defined by failure to locate a physical cause\dots 
\stopquotation

This is just to say that psychiatric epidemiology has to be distinguished from
classical epidemiological in the way data can be collected and inference may
be drawn. Indeed, it is not very easy to design a study where we deliberately
manipulate risk factors, nor when we decide to continue a study that would
affect someone's welfare. Thus, studies are mainly of observational nature per
se, and there we run into problems related to confounding or mediating effects
that are often not easily solvable.

{\bf Section 1} is mainly devoted to the history of psychatric epidemiology
and the new tools developed so far. Most of these statistical apparatus grow
on measurement theory which put emphasis on validity and reliability of the
measurement of mental processes. Such topics are dealt at length by G. Dunn in
his textbook {\em Statistics in Psychiatry} (See next section). Additionally,
case-control and cohort studies have provided much more interesting results
than cross-sectional surveys developed in the eighteen century. These kind
of study design are discussed in the next section, but see
below. Cross-cultural issues are discussed in a separate chapter: V. Patel
mainly points to the comparability of psychiatric disorders measurement in
different countries whose social and cultural setting may interfere with a
direct and clear-cut interpretation of the results.

{\bf Section 2} reviews the basic experimental design used to assess or
uncover significant marker of mental disorders. As shown in the next figure,
this includes: cross-sectional, case-control and cohort or longitudinal
studies, randomized clinical trials, and post-hoc or meta-analysis. In short,
the design and analysis of a study aims at maximizing the precision and
validity of its findings. Avoiding bias or confounding effect is thus the
primary goal of any epidemiologist, especially because most of the studies
under consideration are observational per se. Strict control over experimental
factors or exposure, like in an RCT, are thus generally lacking.

\placefigure
  [here,force]
  [fig:PEP1]
  {none}
  {\externalfigure[PEP_Fig_5-1][width=.5\textwidth]}

Here are the main advantages and drawbacks of each design:
\startitemize
\item {\em Ecological studies}: possible \quote{ecological fallacy} effect.
\item {\em Cross-sectional surveys}: no direct evidence of causality,
  representative sampling frame, response rate, not applicable to rare
  disorders. 
\item {\em Case-control study}: mainly retrospective, subjects defined by
  outcome, can handle multiple exposures, odds-ratio approximately equal to
  relative risk in a cohort study for rare disorders (prevalence < 10\%),
  prone to several form of bias (selection, information, i.e. observer and
  recall). 
\item {\em Cohort study}: longitudinal (prospective) design or historical
  cohort or population cohort, subjects designed by their exposure, not
  well-suited for rare disorders, can handle multiple outcomes/exposures, no
  information bias. 
\item {\em Intervention study}: allocating exposure to subjects and comparing
  the outcome of interest in different random groups, no information bias
  provided trials are double-blind.
\stopitemize

I also reproduce Table 5.1 (page 87) as it summarizes much of the
characteristics of each study design. 

\starttable[|lp(1.5cm)|lp(2cm)|lp(2cm)|lp(2cm)|lp(2cm)|lp(2cm)|]
\HL
\NC \NC Cross-sectional survey \NC Case-control	\NC Cohort \NC Ecological \NC
Clinical trial \SR
\HL
\NC Subject selection \NC Defined population \NC Caseness \NC Exposure \NC
Aggregated data \NC Caseness \AR
\NC Source of bias \NC Selection, Non-response, Information (Recall and
observer) \NC Selection, Information (Recall and observer) \NC Information
(observer only), Loss to follow up (selection) \NC Selection of population,
Ecological fallacy \NC Selection, Information (reduced by blinding) \AR
\NC Probability of confounding \NC Medium \NC Medium \NC Low \NC High \NC Very
low if randomized \AR
\NC Resources \NC Quick and cheap \NC Relatively quick and cheap \NC Lengthy
and expensive \NC Relatively quick and cheap \NC Relatively expensive \AR
\NC Applications \NC Planning services, Mapping secular and geographical
trends, Identifying correlates \NC Rare outcomes, Single outcomes, Multiple
exposures \NC Rare exposure, Single exposures, Multiple outcomes \NC Rare
outcomes, Rare exposures, Multiple exposures, Population exposures such as air
pollution \NC Efficacy of new interventions, Effectiveness of new
interventions, Hypothesis testing, Mechanisms \AR
\NC Measures of effect \NC Prevalence \NC Odds ratio \NC Relative risk \NC
Correlation/ regression coefficient \NC Relative risk/odds ratio/difference
between means \SR
\HL
\stoptable

Probably the most well-known designs are the Case-control and Cohort design
study. Case-control studies are most appropriate when the disorder under study
is (i) rare, (ii) takes a long time to develop after the exposure (sometimes
referred to as the latent period), and (iii) where the exposure is
common. Cohort studies are more appropriate when the disorder is (i) common,
(ii) it does not take long to develop, and (iii) the exposure is rare. They
both come with their own drawbacks and interpreting the results gathered
through such approaches often involves a careful inspection of the
hypothetical causal pathways. 

\useURL[RCT-article][http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1882707&blobtype=pdf][][When to stop a clinical trial]
\useURL[EpiInfo][http://www.cdc.gov/epiinfo][][EpiInfo]
In either case, we need to ensure the proper recording of the data, as well as
appropriate statistical analysis. While the former may be facilitate with
dedicated software\footnote{\from[EpiInfo] is a free package that can handle
  many of the usual statistical routines. It also includes a data editor which
  allows for double data entry comparison. Further, it can handle map files
  (ArcView compatible) and get data stroed in a SQL database.},
care must be taken when analyzing data gathered through complex sampling
frame, like stratified sampling with unequal sampling weights or cluster
sampling. Such designs call for specialized software like SAS or Stata, though
SPSS now has some survey design capabilities (since version 15). S.J. Pocock
gives a nice overview of clinical trials and how to manage them:
\from[RCT-article] (pdf).

\useURL[Revman][http://www.cc-ims.net/RevMan/][][Revman]
\useURL[MetaAnalysis-software][http://www.meta-analysis.com/downloads/PBSoftware.pdf][][Software for meta-analysis and publications bias]
\useURL[MetaAnalysis-art1][http://jama.ama-assn.org/cgi/content/full/283/15/2008][][Meta-analysis of Observational Studies in Epidemiology]
\useURL[MetaAnalysis-art2][http://www.bmj.com/cgi/content/full/323/7304/101][][Investigating and dealing with publication and other biases in meta-analysis]

{\bf Section 3} puts emphasis on the reading of data, in particular on the
relevance of causality in the interpretation of a significant association
between symptoms, biological or genetical markers, and disturbed behavior or
mental trouble. This is probably the most interesting aspect of such studies,
and obvisouly the harder one\dots Many software are available for study
comparison, also known as meta-analysis (e.g. \from[Revman]), but read the
chapter \from[MetaAnalysis-software] (pdf) of {\em Publication Bias in
  Meta-Analysis: Prevention, Assessment and Adjustments}, H. Rothstein,
A.J. Sutton, and M. Borenstein (Eds.), Wiley 2005. There is also this
interesting article, \from[MetaAnalysis-art1] and \from[MetaAnalysis-art2],
written by J.A.C. Sterne and coll. Both are available on-line.
 
\useURL[CONSORT][http://bmj.bmjjournals.com/cgi/content/full/313/7057/570?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&andorexactfulltext=and&searchid=1098792174432_9484&stored_search=&FIRSTINDEX=0&sortspec=relevance&volume=313&firstpage=570&resourcetype=1][][CONSORT statement]
The fundamental question when interpreting research findings is to consider
what observations \quote{mean} and what can be inferred from them. The
\from[CONSORT] also provides useful guidelines for reporting results gained
through RCTs. The diagram below illustrates the checking process that every
critical reviewer may follow in order to ensure that a correct inference can
be done based on the observed results.

\useURL[RS][http://www.iop.kcl.ac.uk/staff/profile/default.aspx?go=10670][][Robert Stewart]
\useURL[RS-article][http://www.aliquote.org/pub/Hill_1965.pdf][][\quote{The environment and disease: association or causation?}]

\from[RS] offers in Chapter 13 an extensive discussion around causal pathways
and how inferential aspects shall be handled. The table reproduced in the next
page (right panel) summarizes the approach promulgated by Hill (1965) which
includes 9 different criteria related to causation effect. His original
article was indeed entitled \from[RS-article] (pdf).

\startcombination[2*1] 
{\externalfigure[PEP_Fig_12-1][width=.48\textwidth]} {} 
{\externalfigure[Hill_1965][width=.48\textwidth]} {}
\stopcombination 

Causation, or causality interpretation, are by far the most difficult aspects
of epidemiological research. Cohort and cross-sectional studies might both
lead to confoundig effects for example. Another set of criteria has been
proposed by H.B. Asher ({\em Causal Modeling}, Sage, 1976), as cited by
S. Menard ({\em Longitudinal Research}, Sage University Paper 76, 1991):

\startitemize[n]
\item The phenomena or variables in question must covary, as indicated for
  example by differences between experimental and control groups or by nonzero
  correlation between the two variables. 
\item The relationship must not be attributable to any other variable or set
  of variables, i.e., it must not be spurious, but must persist even when
  other variables are controlled, as indicated for example by successful
  randomization in an experimental design (no difference between experimental
  and control groups prior to treatment) or by a nonzero partial correlation
  between two variables with other variable held constant. 
\item The supposed cause must precede or be simultnaeous with the supposed
  effect in time, as indicated by the change in the cause occuring no later
  than the associated change in the effect. 
\stopitemize

While the first two criteria can easily be checked using a cross-sectional or
time-ordered cross-sectional study, the latter can only be assessed with
longitudinal data, except for biological or genetic characteristics for which
temporal order can be assume without longitudinal data. Of course, the
situation becomes more complex in case of a non-recursive causal
relationship. To catch up some of the most useful concepts, here is a brief
illustration of the main cause-effect relationships one may encounter in an
observational study: 

\placefigure
  [here,force]
  [fig:PEP4]
  {none}
  {\externalfigure[PEP_Fig_13-2][width=.5\textwidth]}

The interested reader could also refer to Chapter 2 of Rothman's {\em Modern
Epidemiology}\footnote{Now in its 3rd Edition (2008)!} (1998, Lippincott Williams \& Wilkins, 2nd Edition).

Finally, {\bf Section 4} covers additional topics, not usually found in more
classical textbooks, such as genetic epidemiology, health economics or
qualitative research. 

\subject{Additional links}

Here are some useful links about actual survey or health related information systems, as well as various links related to medical statistics.

\useURL[link1][http://ec.europa.eu/health/ph_information/dissemination/reporting/ehss_04_en.htm][][The European Health Survey System]
\useURL[link2][http://www.oecd.org/sante][][OCDE Santé]
\useURL[link3][http://www.medal.org/visitor/login.aspx][][Medical algorithms]
\useURL[link4][http://www.meta-analysis.com/pages/pub_bias.html][][Publications Bias]
\useURL[link5][http://www.consort-statement.org/][][CONSORT, Transparent Reporting of Trials]
\startitemize
\item \from[link1]
\item \from[link2]
\item \from[link3]
\item \from[link4]
\item \from[link5]
\stopitemize
\useURL[PEP1][http://www.aliquote.org/pub/PEP.html][][PEP.html]
\useURL[PEP2][http://www.aliquote.org/pub/PEP.html][][PEP.bib]
Finally, I have compiled some of the most relevant bibliography entries (to my
opinion, of course) in the following BibTeX file: \from[PEP1] (\from[PEP2]).

\subject{Remark}
\useURL[Eurostat][http://ec.europa.eu/eurostat/][][Eurostat]
\useURL[WHO][http://www.who.int/en/][][World Health Organization]
\useURL[GHA][http://www.who.int/globalatlas/][][Global Health Atlas]
When I am looking for some large-scale (aggregated or not) dataset, I usually
search the \from[Eurostat] database which contains data collected for the
European Commission. In the case of biomedical data, a useful resources is the
\from[WHO]. For instance, the \from[GHA] includes
standardized data and statistics for infectious diseases at country, regional,
and global levels. The figure below is taken from their homepage. 

\placefigure
  [here,force]
  [fig:PEP5]
  {none}
  {\externalfigure[GHA][width=.5\textwidth]}


\section{Statistics in Psychiatry}
\useURL[BJP][http://bjp.rcpsych.org/cgi/content/full/178/2/187-a][][The British Journal of
Psychiatry]
\useURL[GD][http://www.medicine.manchester.ac.uk/staff/1470][][Graham Dunn]
Another great and concise book on how to handle medical data. Part of the book
is devoted to instrument reliability and validity. This is nearly what I'm
concerned about in my daily activities, especially reliabilities issues. A
short overview already exists and can be found on \from[BJP]. However, I'd
like to emphasize some of the interpretation issues raised by \from[GD], as
well as some technical aspects related to reliability studies (which forms the
basis of Chapter 2).

I already talk about questionnaire design and QoL assessment, but I think
reliability issues are of major concern when designing survey or clinical
assessments. Indeed, I will focus on inter-rater agreement measure. As pointed
by G. Dunn, most of th reliability statistics found in scientific paper are
derived or can be interpreted from usual intra-class coefficient
(ICC). Basically, ICC provides a way to ascertain how different observer agree
in evaluating some observable characteristics. There are various way to
estimate inter-observer agreement (e.g. see these posts 1 and 2 on MedStats,
or John Uebersax related website). Usually, in the case of quantitative
measurements (e.g. scores), variance components (VC) analysis will be
sufficient to recover inter- and intra-block variations, from which one can
easily derive an inter-observer agreement measure. More sophisticated
techniques can be used as well, for instance Item Response Model [3,4,6] or
Generalizability Theory [1,5], which is basically an extension of VC
analysis. If ordinal scoring is used, Kappa coefficient can be used instead
. In fact, it can be shown that $\kappa$ is asymptotically equivalent to the ICC
estimated from a two-way random-effects ANOVA.

In the following, we will be concerned with the situation where several
observers rate (independently) several subjects. Such study can resort to a
complete randomized block design (CRBD), or to a balanced incomplete block
design (BIBD), in order to minimize the costs and the number of trials. In
this latter case, each subject is only rated by a subset of all the examiners,
and, conversely, each observer only rates some of the subjects. 



\subject{References}
\startcolumns[n=2,rule=on,tolerance=verytolerant]

\startitemize[m]
\item MacMillan, P.D. (2000). Classical, Generalizability, and Multifaceted
  Rasch Detection of Interrater Variability in Large, Sparse Data Sets. {\em
    Journal of Experimental Education}, {\em 68(2)}, 167--190. 

\useURL[1][http://pareonline.net/getvn.asp?v=9&n=4][][www]
\item Stemler, Steven E. (2004). A comparison of consensus, consistency, and
  measurement approaches to estimating interrater reliability. {\em Practical
    Assessment, Research \& Evaluation}, {\em 9(4)}. [\from[1]]  

\useURL[2][http://www.eric.ed.gov:80/ERICWebPortal/custom/portlets/recordDetails/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED345498&ERICExtSearch_SearchType_0=no&accno=ED345498][][www]
\item McNamara, T.F. and Adams, R.J. (1991). Exploring Rater Behaviour with
  Rasch Techniques. {\em Annual Language Testing Research
    Colloquium}. [\from[2]]  

\useURL[3][http://www.pubhealth.ku.dk/upload/application/pdf/f51d6748/Research%20report%2007-6.pdf][][pdf]
\item Petersen, J.H., Larsen, K., and Kreiner, S. (2007). Assessing and quantifying inter-rater variation for dichotomous ratings using a Rasch model. [\from[3]]  

\item Brennan, R.L. (2001). {\em Generalizability Theory}. Springer.

\useURL[4][http://www.umoncton.ca/raicheg/sitemee/ACFAS_MEE_BLAIS.pdf][][pdf]
\item Blais, J.-G. and Loye, N. (2003). {\em Une étude de l'accord et de la
  fidélité inter juges comparant un modèle de la théorie de la
  généralisabilité et un modèle de la famille de Rasch}. Département
  d'administration et fondements de l'éducation, Université de Montréal. [\from[4]]   

\useURL[5][http://www.aare.edu.au/03pap/con03598.pdf][][pdf]
\item Connally, J., Jorgensen, K., Gillis, S., and Griffin, P. (2003). A multi
  source measurement approach to the assessment of higher order
  competencies. {\em Australian Association for Research in Education (AARE)
    Joint Conference}. [\from[5]]      

\useURL[6][http://apm.sagepub.com/cgi/reprint/12/3/321][][pdf]
\item Uebersax, J.S. (1988).  PANEL: Latent Class Modeling of Interrater
  Agreement. {\em Applied Psychological Measurement}, {\em 12}, 321. [\from[6]] 

\useURL[7][http://www.mt-archive.info/MTS-2005-Miller.pdf][][pdf]
\item Miller, K.J. and Vanni, M. (2005). Inter-rater Agreement Measures and
  the Refinement of Metrics in the PLATO MT Evaluation Paradigm. {\em
    Conference Proceedings: the tenth Machine Translation Summit},
  pp. 125--132. [\from[7]] 

\useURL[8][http://www-unix.oit.umass.edu/~bogartz/Interrater%20Agreement.pdf][][pdf]
\item Bogartz, R.S. Interrater Agreement and Combining Ratings. Unpublished
  manuscript. [\from[8]] 
\stopitemize
\stopcolumns
\stoptext