Generalized Estimating Equations

Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with longitudinal and clustered data. Developed specifically to accommodate these data types, the method of Generalized Estimating Equations (GEE) extends the GLM algorithm to accommodate the correlated data encountered in health research, social science, biology, and other related fields. Generalized Estimating Equations provides the first complete treatment of GEE methodology in all of its variations. After introducing the subject and reviewing GLM, the authors examine the different varieties of generalized estimating equations and compare them with other methods, such as fixed and random effects models. The treatment then moves to residual analysis and goodness of fit, demonstrating many of the graphical and statistical techniques applicable to GEE analysis. With its careful balance of origins, applications, relationships, and interpretation, this book offers a unique opportunity to gain a full understanding of GEE methods, from their foundations to their implementation. While equally valuable to theorists, it includes the mathematical and algorithmic detail researchers need to put GEE into practice.

Table of Contents:
INTRODUCTION Notational Conventions A Short Review of Generalized Linear Models Software Exercises MODEL CONSTRUCTION AND ESTIMATING EQUATIONS Independent Data Estimating the Variance of the Estimates Panel Data Estimation Summary Exercises GENERALIZED ESTIMATING EQUATIONS Population-Averaged (PA) and Subject-Specific (SS) Models The PA-GEE for GLMs The SS-GEE for GLMs The GEE2 for GLMs GEEs for Extensions of GLMs Further Developments and Applications Missing Data Choosing an Appropriate Model Summary Exercises RESIDUALS, DIAGNOSTICS, AND TESTING Criterion Measures Analysis of Residuals Deletion Diagnostics Goodness of Fit (Population-Averaged Models) Testing Coefficients in the PA-GEE Model Assessing the MCAR Assumption of PA-GEE Models Summary Exercises PROGRAMS AND DATASETS Programs Datasets References Author Index Subject Index

Review :
"These are well written chapters . The book contains challenging problems in exercises and is suitable to be a text book 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, Vol. 75, No. 2, Feb. 2005 "[The book] is comprehensive and covers much useful material with formulas presented in detailthis is a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field." 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