Cited by 387Statistical Methods and Bayesian InferenceSpatial and Panel Data AnalysisStatistical Methods and Applications

Review of Software to Fit Generalized Estimating Equation Regression Models

Horton, Nicholas J., Lipsitz, Stuart R.·1999·The American Statistician

Abstract

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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Crossref · 10.1080/00031305.1999.10474451

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Authors
Horton, N. J., & Lipsitz, S. R.
Affiliations
Harvard University (US)
Venue
The American Statistician
Published
1999-05
Volume
53
Issue
2
Pages
160-169
ISSN
  • 0003-1305 (ISSN-L)
  • 1537-2731
OpenAlex
W1979231358
Cited by
387
References
24
Citations by year
2026: 3 · 2025: 10 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 11 · 2020: 6 · 2019: 11 · 2018: 11 · 2017: 9 · 2016: 17 · 2015: 10 · 2014: 18 · 2013: 12 · 2012: 24
FWCI
11.06
Type
journal article
Language
en
Open access
closed
Locations
The American Statistician (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
generalized estimating equations, software implementation, statistical software comparison, SAS, Stata, S-PLUS, longitudinal data, clustered data, correlated observations, generalized linear models, discrete outcomes, user interface, statistical accuracy

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