Table of Contents
Advances in Statistics
Volume 2014, Article ID 303728, 11 pages
Review Article

Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments

Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA

Received 17 March 2014; Revised 29 October 2014; Accepted 16 November 2014; Published 1 December 2014

Academic Editor: Chin-Shang Li

Copyright © 2014 Ming Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of potential research interests regarding GEE are provided in the end.