Table of Content
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.
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