Statistical Methods in Psychiatry and Related Fields

Ralitza Gueorguieva is a Senior Research Scientist at the Department of Biostatistics, Yale School of Public Health. She has more than 20 years experience in statistical methodology development and collaborations with psychiatrists and other researchers, and is the author of over 130 peer-reviewed publications.

• IntroductionTraditional Methods for Analysis of Longitudinal and Clustered DataLinear Mixed Models for Longitudinal and Clustered DataLinear Models for Non-normal OutcomesNonparametric Methods for the Analysis of Repeatedly Measured DataPost-hoc Analysis and Adjustments for Multiple ComparisonsHandling of Missing Data and Dropout in Longitudinal StudiesControlling for Covariates in Studies with Repeated MeasuresAssessment of Moderator and Mediator EffectsMixture Models for Trajectory AnalysesStudy Design and Sample Size CalculationsSummary and Further Readings

"This is a comprehensive text describing a wide-range of statistical techniques, from basic to advanced, applicable to data generated from the field of Mental Health research. The field of Psychiatry is one of the very core subjects of Mental Health, along with Clinical Psychology and Psychiatric SocialWork. Hence, it is in fact an amalgamation of many cross linking fields of the social sciences. Therefore, coming out with a book that addresses various dimensions of science is a remarkable achievement. … [T]he book has three unique selling points – addressing the issues of the different natures of data in psychiatry, post hoc analyses and adjustments for multiple comparisons, and study design and sample size determination. … I very strongly advocate that researchers/academics have this book with them or in their library."—Chandra Bhushan Tripathi, in ISCB News, December 2018"This book will reach a wide audience since it gives a non-technical and comprehensible introduction for non-experts to a complicated topic, with a series of worked-through examples, while at the same time it provides the applied statistician with thorough guidance to the analysis of longitudinal data, in the traditional normal distribution setting as well as for non-normal distributions (binary, Poisson etc.). It also contains insightful discussions on more advanced topics, with good references for further reading. The book stands out in the discussion on multiple testing, with good advice in a jungle of possibilities and in the handling of missing values (a comprehensible explanation of the problems and pitfalls, together with a sober guidance to avoiding such pitfalls in various circumstances). Also, the section on causality is probably the best I ever came across.The guidance and summary sections at the end of each chapter will serve as a reminder on the important hints for this particular topic. An extremely nice addition to the book is the accompanying home pages with SAS programs for the various analyses performed. The combination of non-technical verbal explanations of the models, combined with the precise analysis provided by the code, makes the book very well suited for teaching as well as for self study."—Lene Theil Skovgaard, Section of Biostatistics, University of Copenhagen"This is an extremely useful book, especially for data analysts who want the "nuts and bolts" of conducting longitudinal and clustered data analysis. The logic and necessary steps are carefully laid out and explained in great detail. Also, there are several chapters that include material not often included in books on longitudinal data analysis, for example the treatment of trajectory and growth mixture models, mediator and moderator effects, study design and sample size considerations. The examples focus on psychiatric studies, so this book will be of most interest to researchers in psychiatry, psychology, and mental health. However, those in other fields can also gain a great deal by considering this book, which is exemplary in its thoroughness and clarity. Highly recommended for anyone wanting to learn about statistical methods for longitudinal and clustered data, and even the experts can learn a great deal by the careful treatment of these topics that Dr. Gueorguieva has provided."—Donald Hedeker, University of Chicago