Data collected in psychiatry and related fields are complex because outcomes are rarely directly observed, there are multiple correlated repeated measures within individuals, there is natural heterogeneity in treatment responses and in other characteristics in the populations.
Simple statistical methods do not work well with such data. More advanced statistical methods capture the data complexity better, but are difficult to apply appropriately and correctly by investigators who do not have advanced training in statistics.
This book presents, at a non-technical level, several approaches for the analysis of correlated data: mixed models for continuous and categorical outcomes, nonparametric methods for repeated measures and growth mixture models for heterogeneous trajectories over time.
Separate chapters are devoted to techniques for multiple comparison correction, analysis in the presence of missing data, adjustment for covariates, assessment of mediator and moderator effects, study design and sample size considerations. The focus is on the assumptions of each method, applicability and interpretation rather than on technical details.
- Provides an overview of intermediate to advanced statistical methods applied to psychiatry.
- Takes a non-technical approach with mathematical details kept to a minimum.
- Includes lots of detailed examples from published studies in psychiatry and related fields.
- Software programs, data sets and output are available on a supplementary website.
The intended audience are applied researchers with minimal knowledge of statistics, although the book could also benefit collaborating statisticians.
The book, together with the online materials, is a valuable resource aimed at promoting the use of appropriate statistical methods for the analysis of repeated measures data.
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.
Table of Contents
Traditional Methods for Analysis of Longitudinal and Clustered Data
Linear Mixed Models for Longitudinal and Clustered Data
Linear Models for Non-normal Outcomes
Nonparametric Methods for the Analysis of Repeatedly Measured Data
Post-hoc Analysis and Adjustments for Multiple Comparisons
Handling of Missing Data and Dropout in Longitudinal Studies
Controlling for Covariates in Studies with Repeated Measures
Assessment of Moderator and Mediator Effects
Mixture Models for Trajectory Analyses
Study Design and Sample Size Calculations
Summary and Further Readings
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.
"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 ho