Main description:

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.

Author Website:Data sets and programs used in the book are available at http://www.medstat.jp/downloadrepeatedcrc.html

Contents:

Table of Contents

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

Discussion

Analysis of variance models

Introduction

Analysis of variance model

Change from baseline

Split-plot design

Selecting a good _t covariance structure using SAS

Heterogeneous covariance

ANCOVA-type models

From ANOVA models to mixed-effects repeated measures models

Introduction

Shift 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

Introduction

Handling 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

Discussion

Mixed-effects logistic regression models

The Respiratory data with 1:4 design

Odds ratio

Logistic 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

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 V

Sample 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

Solutions to Exercises