Models for Discrete Longitudinal Data

From the reviews: "Strengths of this book include its breadth of topics, excellent organization and clarity of writing...I highly recommend this book to my colleagues and students." -Justine Shults for the Journal of Biopharmaceutical Statistics, Issue 3, 2006 "Models for Discrete Longitudinal Data is an excellent choice for any statistician with an interest in analyzing discrete longitudinal data. It covers all of the theoretical and applied aspects in this area and is organized in such a way to serve as a handy reference guide for applied statisticians, especially those in biomedical fields. I learned a great deal from this book, and I recommend it highly to others." -John Williamson for the Journal of the American Statistical Association, September 2006 "This book complements Verbeke and Molenberghs (2000), which focused on models based on the multivariate normal distribution. ... This book covers the alternative models and approaches in a methodical and accessible manner. The emphasis in the book is on presenting methods for solving practical problems, and the authors succeed admirably in this. ... The material is clearly presented ... . This book is very welcome, and will undoubtedly prove to be useful and influential." (B. J. T. Morgan, Short Book Reviews, Vol. 26 (2), 2006) "This book provides a comprehensive treatment of modeling approaches for non-Gaussian repeated measures ... . the book shows how the different approaches can be implemented within the SAS software package. The text is so organized that the reader can skip the software-oriented chapters and sections without breaking the logical flow. ... It is a very important, modern and useful book for statisticians." (T. Postelnicu, Zentralblatt MATH, Vol. 1093 (19), 2006) "This book ... concentrates on models for non-normally distributed longitudinal data, like binary or categorical data. ... The book under review is a comprehensive collection of latest models for non-normally distributed longitudinal data. ... Models for Discrete Longitudinal Data addresses interested (and experienced) students and lectures as well as practitioners looking for solutions of everyday problems." (K. Webel, Advances in Statistical Analysis, Vol. 91 (2), 2007)

• Motivating Studies.- Generalized Linear Models.- Linear Mixed Models for Gaussian Longitudinal Data.- Model Families.- The Strength of Marginal Models.- Likelihood-based Marginal Models.- Generalized Estimating Equations.- Pseudo-Likelihood.- Fitting Marginal Models with SAS.- Conditional Models.- Pseudo-Likehood.- From Subject-specific to Random-effects Models.- The Generalized Linear Mixed Model (GLMM).- Fitting Generalized Linear Mixed Models with SAS.- Marginal versus Random-effects Models.- The Analgesic Trial.- Ordinal Data.- The Epilepsy Data.- Non-linear Models.- Pseudo-Likelihood for a Hierarchical Model.- Random-effects Models with Serial Correlation.- Non-Gaussian Random Effects.- Joint Continuous and Discrete Responses.- High-dimensional Joint Models.- Missing Data Concepts.- Simple Methods, Direct Likelihood, and Weighted Generalized Estimating Equations.- Multiple Imputation and the Expectation-Maximization Algorithm.- Selection Models.- Pattern-mixture Models.- Sensitivity Analysis.- Incomplete Data and SAS.