کتابخانه دیجیتال فوركيا

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Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials


Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials

Title: Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials | Author(s): Toshiro Tango  | Publisher: CRC  | Year: 2017 | Language: english | Pages : 359 | ISBN: 978-1-4987-4789-9 | Size: 8 MB | Extension: pdf

 

Features

Highlights a new S:T repeated measures design as a practical framework of RCT design

Presents in-depth treatments of mixed models useful for the practical design of randomized controlled trials depending on the study purpose

Illustrates most analyses with real data using the SAS procedures MIXED and GLIMMIX with source programs so that the results in the book are reproducible

Provides detailed steps for determination of sample size including Monte Carlo simulations

Summary

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.

 

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

 

 


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