Description:
This second edition of a bestseller incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Along with doubling the number of end-of-chapter exercises, this edition offers more thorough coverage of hypothesis testing and diagnostics, expands discussion of various models associated with GEE, and provides a new presentation of model selection procedures. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined.
Review Quotes:
"Overall, I found this to be a very useful book on GEE, and would recommend it to anyone planning to use GEE models in their data analysis. Both the theory and practical aspects of constructing and analysing such models is covered. Inclusion of code for many of the analyses is an excellent feature."
--Ken J. Beath, Macquarie University, Australia, Australian and New Zealand Journal of Statistics, April 2017
"The second edition ... adds a few new topics related to various extensions of GEE ... [and replaces] outdated S-PLUS codes with R scripts. Also, the number of exercises increased significantly ... . For those who want to use this book in the classroom, including me, having extra exercise sets is certainly a welcome addition. ... One main strength of this book is its comprehensive coverage of Stata implementation of the GEE. ... a valuable reference and is particularly useful for practitioners. It can serve as supplemental reading in longitudinal data analysis classes as well."
--Woncheol Jang, Biometrics, September 2013
Praise for the First Edition: "... well-written chapters ... . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. ... I enjoyed reading [this book] and recommend [it] very highly to the statistical community."
--Journal of Statistical Computation and Simulation, February 2005
--Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004 "Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM).
The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing.
Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology."
--Journal of the American Statistics Association, March 2004 "Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas."
--Technometrics, 2003