(Not in Generalized Estimating Equations, Second Edition
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$89.95$80.96 - Hardback: 320 pages
- Published: December 2012
- ISBN: 978-1-4398811-3-2
- Publisher: Chapman and Hall/CRC
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- By James W. Hardin, and Joseph M. Hilbe.
This popular book covers generalized estimating equations (GEEs), an extension of the generalized linear model when correlation is unknown. It remains the only book to focus specifically on this important method used in the analysis of longitudinal data. This edition features new methods, particularly regarding correlation structure and the interpretation of GEE models. It also presents updated computational material and now includes both Stata and R code for all the examples as well as corresponding SAS code. All data sets and code are available at CRC Press Online.
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
Reviews
Praise for the First Edition:
These are … 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
[The book] is comprehensive and covers much useful material with formulas presented in detail … a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field.
—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
