Features

• Provides a comprehensive treatment of linear models, integrating the traditional linear models with generalized and mixed extensions
• Presents background theory and methods, along with applications and case studies
• Discusses current controversies in the field
• Implements methods using SAS and R

Summary

This text covers statistical modeling using generalized linear mixed models (GLMMs) as the organizing tool. After an overarching introduction to modeling from a contemporary perspective, the book presents the main theory and methods used for setting up estimation and inference for GLMMs. It also describes the major classes of applications with case studies from biostatistics and epidemiology. SAS is included throughout while R is used when SAS does not work well with the GLMM.