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.
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