Introduction.- Motivating Studies.- Generalized Linear Models.- Linear Mixed Models for Gaussian Longitudinal Data.- Model Families.- The Strength of Marginal Models.- Likelihood-based Models.- Generalized Estimating Equations.- Pseudo-likelihood.- Fitting Marginal Models with SAS.- Conditional Models.- Pseudo-likehood.- From Subject-Specific to Random-Effects Models.- Generalized Linear Mixed Models (GLMM).- Fitting Generalized Linear Mixed Models with SAS.- Marginal Versus Random-Effects Models.- Ordinal Data.- The Epilepsy Data.- Non-linear Models.- Psuedo-likelihood for a Hierarchical Model.- Random-effects Models with Serial Correlation.- Non-Gaussian Random Effects.- Joint Continuous and Discrete Responses.- High-dimensional Multivariate Repeated Measurements.- Missing Data Concepts.- Simple Methods, Direct Likelikhood and WGEE.- Multiple Imputation and the Expectation-Maximization Algorithm.- Selection Models.- Pattern-mixture Models.- Sensitivity Analysis.- Incomplete Data and SAS.
