Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS® Reviews

"Dr. Vonesh has written a clear and comprehensive treatise on the analysis of correlated data using SAS. In particular, he describes generalized linear and nonlinear models that address four types of correlated data encountered in statistical practice: repeated measurements including longitudinal data, clustered data, spatially correlated data, and multivariate data.

Dr. Vonesh provides a thorough development of the theoretical aspects of each statistical model with a rich set of references. More important for the practitioner is the inclusion of numerous data sets and SAS programs to illustrate the statistical models and accompanying analytical methods. Dr. Vonesh even provides selected SAS macros that he himself has written to supplement the available SAS procedures.

This book will be extremely valuable to any practitioner who analyzes correlated data sets. Graduate programs in biostatistics and other branches of applied statistics should consider the adoption of this book on its own, or as a supplement, for an advanced course in statistical methods."

Vernon M. Chinchilli
Penn State Hershey College of Medicine
"This book is a very advanced treatment on how to analyze complex mixed effects datasets with SAS, covering marginal or population-averaged models, and mixed effects models obtained by adding subject or cluster-specific random effects to the marginal model…. The book is useful for researchers who already know how to fit such models in SAS, but want to improve their analysis."

--Technometrics

Back to Top