Bayesian methods for repeated measures (2015) Pdf Free Download

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Bayesian methods are being used successfully in many areas of scientific investigation. For example, at the University of Texas MD Anderson Cancer Center, Houston, Texas, Bayesian sequential stopping rules are routinely employed in the design and analysis of clinical trials, and they are also used in diagnostic medicine in estimating the accuracy of diagnostic tests as well as for screening large populations for various chronic diseases. Of course, Bayesian inference is prevalent in other areas of science, including astronomy, engineering, biology, and cryptology.
This book is intended to be a textbook for graduate students in statistics (including biostatistics) and as a reference for consulting statisticians.
It will be an invaluable resource especially for those involved in biostatistics, where use of repeated measures is common. The book adopts a practical approach and includes the WinBUGS code necessary to implement posterior analysis. Much of the book is devoted to inferential methods for the analysis of repeated measures for various areas of scientific endeavor. The student should have completed a one-year course in mathematical statistics and a one-year course in regression methods. The analysis of repeated measures is based on linear and nonlinear regression models, which incorporate correlation between observations on the same individual; therefore the student should have completed at least one course in regression analysis.
Consulting statisticians will find that this book is a valuable resource. The book will supplement other books such as Applied Longitudinal Analysis by Fitzmaurice, Laird, and Ware and Statistical Methods for the Analysis of Repeated Measurements by Davis. These two references are not Bayesian; thus, the present book is unique and presents methods that take advantage of prior information based on previous similar studies. Another feature of the book is that all the computing and analysis is based on the WinBUGS package, which provides the user a platform that efficiently uses prior information. Many of the ideas in this volume go beyond the standard non-Bayesian books.

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