Generalized Estimating Equations

Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book’s website. This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentation of hypothesis testing and diagnostics, covering competing hierarchical models; and a more detailed examination of previously discussed subjects. Along with doubling the number of end-of-chapter exercises, this edition expands discussion of various models associated with GEE, such as penalized GEE, cumulative and multinomial GEE, survey GEE, and quasi-least squares regression. It also offers a thoroughly new presentation of model selection procedures, including the introduction of an extension to the QIC measure that is applicable for choosing among working correlation structures. See Professor Hilbe discuss the book.

Introduction Notational Conventions and AcronymsA Short Review of Generalized Linear Models Software Exercises Model Construction and Estimating EquationsIndependent Data Estimating the Variance of the Estimates Panel Data Estimation Summary Exercises R code for Selected Output Generalized Estimating EquationsPopulation-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 R Code for Selected Output Residuals, Diagnostics, and TestingCriterion 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

James W. Hardin is the Division Director of Biostatistics and an associate professor in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also an affiliated faculty in the Institute for Families in Society. Professor Hardin was the initial author of Stata’s xtgee command and has authored numerous articles and software applications related to GEE and associated models. Professor Hilbe and he have authored three editions of the popular Generalized Linear Models and Extensions and co-authored Stata’s current glm command. He has also co-authored (with P. Good) four editions of the well-accepted Common Errors in Statistics (and How to Avoid Them). Joseph M. Hilbe is a Solar System Ambassador with the Jet Propulsion Laboratory, an adjunct professor of statistics at Arizona State University, and an Emeritus Professor at the University of Hawaii. An elected fellow of the American Statistical Association and elected member of the International Statistical Institute (ISI), Professor Hilbe is president of the International Astrostatistics Association as well as chair of the ISI Sports Statistics and Astrostatistics committees. He has authored two editions of the bestseller Negative Binomial Regression, Logistic Regression Models, and Astrostatistical Challenges for the New Astronomy. He has also co-authored Methods of Statistical Model Estimation (with A. Robinson), Quasi-Least Squares Regression (with J. Shults), and R for Stata Users (with R. Muenchen).