Overview

Analyze 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, and the most important topic for repeated measures studies: linear models. It presents an in-depth explanation of estimating the mean profile for repeated measures studies, discusses choosing and estimating the covariance structure of the response, and expands the representation of a repeated measure to general mixed linear models. The author also explains the Bayesian analysis of categorical response data in a repeated measures study, Bayesian analysis for repeated measures when the mean profile is nonlinear, and a Bayesian approach to missing values in the response variable.

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9781138894044

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Reviews

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

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 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 This book is rich in illustrative examples and detailed WinBUGS code for analyzing real-world data, while providing thorough insight in the underlying theory of Bayesian methods for the analysis of repeated measures data. This makes the book a practical guide, and a great resource for learning the theory and practice of Bayesian methods for repeated measures for students and applied statisticians. ~Hao Zhang

Author Information

Lyle D. Broemeling has 30 years of experience as a biostatistician. He has been a professor at the University of Texas Medical Branch at Galveston, the University of Texas School of Public Health at Houston, and the University of Texas MD Anderson Cancer Center. He is also the author of several books, including Bayesian Methods in Epidemiology. His research interests include the analysis of repeated measures and Bayesian methods for assessing medical test accuracy and inter-rater agreement.

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