Bayesian Methods for Repeated Measures

"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

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.

Introduction to the Analysis of Repeated Measures Introduction Bayesian Inference Bayes's Theorem Prior Information Posterior Information Posterior Inference Estimation Testing Hypotheses Predictive Inference The Binomial Forecasting from a Normal Population Checking Model Assumptions Sampling from an Exponential, but Assuming a Normal Population Poisson Population Measuring Tumor Size Testing the Multinomial Assumption Computing Example of a Cross-Sectional Study Markov Chain Monte Carlo Metropolis Algorithm Gibbs Sampling Common Mean of Normal Populations An Example Additional Comments about Bayesian Inference WinBUGS Preview Exercises Review of Bayesian Regression Methods Introduction Logistic Regression Linear Regression Models Weighted Regression Nonlinear Regression Repeated Measures Model Remarks about Review of Regression Exercises Foundation and Preliminary Concepts Introduction An Example Notation Descriptive Statistics Graphics Sources of Variation Bayesian Inference Summary Statistics Another Example Basic Ideas for Categorical Variables Summary Exercises Linear Models for Repeated Measures and Bayesian Inference Introduction Notation for Linear Models Modeling the Mean Modeling the Covariance Matrix Historical Approaches Bayesian Inference Another Example Summary and Conclusions Exercises Estimating the Mean Profile of Repeated Measures Introduction Polynomials for Fitting the Mean Profile Modeling the Mean Profile for Discrete Observations Examples Conclusions and Summary Exercises Correlation Patterns for Repeated Measures Introduction Patterns for Correlation Matrices Choosing a Pattern for the Covariance Matrix More Examples Comments and Conclusions Exercises General Mixed Linear Model Introduction and Definition of the Model Interpretation of the Model General Linear Mixed Model Notation Pattern of the Covariance Matrix Bayesian Approach Examples Diagnostic Procedures for Repeated Measures Comments and Conclusions Exercises Repeated Measures for Categorical Data Introduction to the Bayesian Analysis with a Dirichlet Posterior Distribution Bayesian GEE Generalized Mixed Linear Models for Categorical Data Comments and Conclusions Exercises Nonlinear Models and Repeated Measures Nonlinear Models and a Continuous Response Nonlinear Repeated Measures with Categorical Data Comments and Conclusion Exercises Bayesian Techniques for Missing Data Introduction Missing Data and Linear Models of Repeated Measures Missing Data and Categorical Repeated Measures Comments and Conclusions Exercises References