Front cover image for Models for discrete longitudinal data

Models for discrete longitudinal data

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.
Print Book, English, ©2006
Springer, New York, ©2006
xxii, 683 pages : illustrations ; 24 cm
9780387251448, 0387251448
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
Fitting marginal models with SAS
Conditional models
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