Review of Software to Fit Generalized Estimating Equation Regression Models

Researchers are often interested in analyzing data that arise from a longitudinal or clustered design. Although there are a variety of standard likelihood-based approaches to analysis when the outcome variables are approximately multivariate normal, models for discrete-type outcomes generally requir...

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Bibliographic Details
Published inThe American statistician Vol. 53; no. 2; pp. 160 - 169
Main Authors Horton, Nicholas J., Lipsitz, Stuart R.
Format Journal Article
LanguageEnglish
Published Alexandria, VA Taylor & Francis Group 01.05.1999
American Statistical Association
Subjects
Computer software
Computer software for statistical analysis
Consistent estimators
Correlations
Covariance matrices
Datasets
Estimators
Exact sciences and technology
Generalized estimating equations
Linear inference, regression
Mathematical models
Mathematics
Missing data
Modeling
Multivariate analysis
Nonparametric inference
Parametric models
Probability and statistics
Regression analysis
Sciences and techniques of general use
Software
Statistical analysis
Statistical Computing Software Reviews
Statistical variance
Statistics
Statistical regression
Fitting
Generalized equation
Estimating equation
Statistical estimation
Software
Review
Linear estimation
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Summary:Researchers are often interested in analyzing data that arise from a longitudinal or clustered design. Although there are a variety of standard likelihood-based approaches to analysis when the outcome variables are approximately multivariate normal, models for discrete-type outcomes generally require a different approach. Liang and Zeger formalized an approach to this problem using generalized estimating equations (GEEs) to extend generalized linear models (GLMs) to a regression setting with correlated observations within subjects. In this article, we briefly review GLM, the GEE methodology, introduce some examples, and compare the GEE implementations of several general purpose statistical packages (SAS, Stata, SUDAAN, and S-Plus). We focus on the user interface, accuracy, and completeness of implementations of this methodology.
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ISSN:0003-1305
1537-2731
DOI:10.1080/00031305.1999.10474451