Models for Discrete Longitudinal Data

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow.

The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004 Includes supplementary material: sn.pub/extras

Zusammenfassung

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book.

Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package.

The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

Inhalt
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.