Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application of generalized linear mixed models and its related models in the statistical design and analysis of repeated measures from randomized controlled trials. The author introduces a new repeated measures design called S:T design combined with mixed models as a practical and useful framework of parallel group RCT design because of easy handling of missing data and sample size reduction. The book emphasizes practical, rather than theoretical, aspects of statistical analyses and the interpretation of results. It includes chapters in which the author describes some old-fashioned analysis designs that have been in the literature and compares the results with those obtained from the corresponding mixed models.
The book will be of interest to biostatisticians, researchers, and graduate students in the medical and health sciences who are involved in clinical trials.
Data sets and programs used in the book are available at http://www.medstat.jp/downloadrepeatedcrc.html
Table of Contents
Table of ContentsIntroduction Repeated measures design Generalized linear mixed models Model for the treatment effect at each scheduled visit Model for the average treatment effect Model for the treatment by linear time interaction Superiority and non-inferiority Naive analysis of animal experiment data Introduction Analysis plan I Analysis plan II each time point Analysis plan III - analysis of covariance at the last time point DiscussionAnalysis of variance models Introduction Analysis of variance model Change from baseline Split-plot designSelecting a good _t covariance structure using SAS Heterogeneous covariance ANCOVA-type modelsFrom ANOVA models to mixed-effects repeated measures models IntroductionShift to mixed-effects repeated measures models ANCOVA-type mixed-effects models Unbiased estimator for treatment effects Illustration of the mixed-effects models Introduction The Growth data Linear regression model Random intercept model Random intercept plus slope model Analysis using The Rat data Random intercept Random intercept plus slope Random intercept plus slope model with slopes varying over time Likelihood-based ignorable analysis for missing data IntroductionHandling of missing data Likelihood-based ignorable analysis Sensitivity analysis The Growth The Rat data MMRM vs. LOCF Mixed-effects normal linear regression models Example: The Beat the Blues data with 1:4 design Checking missing data mechanism via a graphical procedure Da
Toshiro Tango is the Director of Center for Medical Statistics, Tokyo. His research interests include various aspects of biostatistics including design and analysis of clinical trials and spatial epidemiology. He has served as associate editor for several journals including Biometrics and Statistics in Medicine, and is the author of Statistical Methods for Disease Clustering.
"The main focus of this book is to introduce the generalized linear mixed-effects models with S:T repeated measures design, which provide a flexible and powerful tool to deal with longitudinal data with heterogeneity or variability among subject-specific responses and missing data. This book illustrates theoretical methodologies with a focus on the practicality with a wealth of real-life examples making it easy to understand the topics. It is well organized and contains SAS codes and outputs as useful references. In summary, this is an excellent book with a very good selection of examples. It is clearly written and is enjoyable to read."
~Misoo C. Ellison, Merck & Co., Inc., Kenilworth, NJ