This book provides the application of generalized linear mixed-effects models and its related models in the statistical design and analysis of repeated measures adopted in randomized controlled trials. With increasing concerns about intra-patient variability of treatment effects, the traditional ANCOVA-type methods can no longer cope with these
This book provides the application of generalized linear mixed-effects models and its related models in the statistical design and analysis of repeated measures adopted in randomized controlled trials. With increasing concerns about intra-patient variability of treatment effects, the traditional ANCOVA-type methods can no longer cope with these
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.
Inhaltsangabe
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
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
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