Description
Book SynopsisAnalyze Repeated Measures Studies Using Bayesian Techniques
Going beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific areas, especially biostatistics.
The author takes a practical approach to the analysis of repeated measures. He bases all the computing and analysis on the WinBUGS package, which provides readers with a platform that efficiently uses prior information. The book includes the WinBUGS code needed to implement posterior analysis and offers the code for download online.
Accessible to both graduate students in statistics and consulting statisticians, the book introduces Bayesian regression techniques, preliminary concepts and techniques fundamental to the analysis of repeated measures,
Trade Review
"The book treats these topics from a Bayesian perspective using WinBugs as the software of choice. The WinBugs code is available on a website and can be used as the reader progresses through the book. The worked examples are often from biostatistics. The intended audience is “graduate students in statistics (including biostatistics) and as a reference for consulting statisticians.”While there are other excellent books on Repeated Measures models, this book is unique in adopting a Bayesian perspective. The book is comprehensive."
~David E. Booth, Kent State University
"The book will be especially useful for clinical researchers, epidemiologists, and other researchers focused on data analysis and seeking to apply Bayesian methods. Useful computer codes and worked examples are provided. Moreover, the book also has utility as a general exposition of data and graph analytic approaches to longitudinal data."
~Peter Congdon, Biometric Journal
"The book treats these topics from a Bayesian perspective using WinBugs as the software of choice. The WinBugs code is available on a website and can be used as the reader progresses through the book. The worked examples are often from biostatistics. The intended audience is “graduate students in statistics (including biostatistics) and as a reference for consulting statisticians.”While there are other excellent books on Repeated Measures models, this book is unique in adopting a Bayesian perspective. The book is comprehensive."
~David E. Booth, Kent State University
"The book will be especially useful for clinical researchers, epidemiologists, and other researchers focused on data analysis and seeking to apply Bayesian methods. Useful computer codes and worked examples are provided. Moreover, the book also has utility as a general exposition of data and graph analytic approaches to longitudinal data."
~Peter Congdon, Biometric Journal
Table of ContentsIntroduction to the Analysis of Repeated Measures. Review of Bayesian Regression Methods. Foundation and Preliminary Concepts. Linear Models for Repeated Measures and Bayesian Inference. Estimating the Mean Profile of Repeated Measures. Correlation Patterns for Repeated Measures. General Linear Mixed Model. Repeated Measures for Categorical Data. Nonlinear Models and Repeated Measures. Bayesian Techniques for Missing Data.
