Features
Provides an overview of the theory and applications of GEEs
Adopts a practical approach with many examples
Contains new methods and examples
Includes theoretical details in the appendices
Offers Stata, SAS, and R code for the examples, with the code and data sets available online
Summary
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.

Table of Contents
Introduction
Notational Conventions and Acronyms
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
R code for Selected Output

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
R Code for Selected Output

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