Models for Discrete Longitudinal Data (Hardcover)

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.