Repeated measures design with generalized linear mixed models for randomized controlled trials

Content: Introduction 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 Data format for analysis using SAS Models for the treatment effect at each scheduled visit Model I: Random intercept model Model II: Random intercept plus slope model Model III: Random intercept plus slope model with slopes varying over time Analysis using SAS Models for the average treatment effect Model IV: Random intercept model Model V: Random intercept plus slope model Analysis using SAS Heteroscedastic models Models for the treatment by linear time interaction Model VI: Random intercept model Model VII: Random intercept plus slope model Analysis using SAS Checking the goodness-of-_t of linearity ANCOVA-type models adjusting for baseline measurement Model VIII: Random intercept model for the treatment effect at each visit Model IX: Random intercept model for the average treatment effect Analysis using SAS Sample size Sample size for the average treatment effect Sample size assuming no missing data Sample size allowing for missing data Sample size for the treatment by linear time interaction DiscussionMixed-effects logistic regression models The Respiratory data with 1:4 design Odds ratioLogistic regression models Models for the treatment effect at each scheduled visit Model I: Random intercept model Model II: Random intercept plus slope model Analysis using SAS Models for the average treatment effect Model IV: Random intercept modelModel V: Random intercept plus slope model Analysis using SAS Models for the treatment by linear time interaction Model VI: Random intercept model Model VII: Random intercept plus slope model Analysis using SAS Checking the goodness-of-_t of linearity ANCOVA-type models adjusting for baseline measurement Model VIII: Random intercept model for the treatment effect at each visit Model IX: Random intercept model for the average treatment effect Analysis using SAS The daily symptom data with 7:7 design Models for the average treatment effect Analysis using SAS Sample size Sample size for the average treatment effect Sample size for the treatment by linear time interaction Mixed-effects Poisson regression models The Epilepsy data with 1:4 design Rate Ratio Poisson regression models Models for the treatment effect at each scheduled visit Model I: Random intercept model Model II: Random intercept plus slope model Analysis using SAS Models for the average treatment effect Model IV: Random intercept model Model V: Random intercept plus slope model Analysis using SAS Models for the treatment by linear time interaction Model VI: Random intercept model Model VII: Random intercept plus slope model Analysis using SAS Checking the goodness-of-_t of linearity ANCOVA-type models adjusting for baseline measurement Model VIII: Random intercept model for the treatment effect at each visit Model IX: Random intercept model for the average treatment effect Analysis using SAS Sample size Sample size for the average treatment effect Sample size for Model Sample size for Model VSample size for the treatment by linear time interaction Bayesian approach to generalized linear mixed models Introduction Non-informative prior and credible interval Markov Chain Monte Carlo methods WinBUGS and OpenBUGS Getting started Bayesian model for the mixed-effects normal linear regression Model V Bayesian model for the mixed-effects logistic regression Model IV Bayesian model for the mixed-effects Poisson regression Model V Latent pro_le models - classification of individual response pro_les Latent pro_le models Latent pro_le plus proportional odds model Number of latent pro_les Application to the Gritiron Data Latent pro_le models R, S-Plus and OpenBUGS programs Latent pro_le plus proportional odds models Comparison with the mixed-effects normal regression models Application to the Beat the Blues Data Applications to other trial designs Trials for comparing multiple treatments Three-arm non-inferiority trials including a placebo Background Hida-Tango procedure Generalized linear mixed-effects models Cluster randomized trials Three-level models for the average treatment effect Three-level models for the treatment by linear time interaction