SAS for Mixed Models, Second Edition, 2nd Edition

Generalized Linear Mixed Models

14

14.1 Introduction

14.2 Two Examples to Illustrate When Generalized Linear Mixed Models Are Needed

14.2.1 Binomial Data in a Multi-center Clinical Trial

14.2.2 Count Data in a Split-Plot Design

14.3 Generalized Linear Model Background

14.3.1 Fixed Effects Generalized Linear Models

14.3.2 Probability Distributions

14.3.3 Link Functions and Variance Structure

14.3.4 Predicting Means from the Inverse Link Function

14.3.5 Deviance

14.3.6 Inference Using Estimable Functions

14.4 From GLMs to GLMMs

14.4.1 Incorporating Random Effects

14.4.2 The Pseudo-likelihood Approach

14.4.3 The Role of the Scale Parameter

14.4.4 Estimation Methods in PROC GLIMMIX

14.5 Example: Binomial Data in a Multi-center Clinical Trial ...