Models for discrete longitudinal data

Introduction

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 WGEE

Multiple imputation and the EM algorithm

Selection models

Pattern-mixture models

Sensitivity analysis

Incomplete data and SAS