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Review of Software to Fit Generalized Estimating Equation Regression Models
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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“Robust (Huber-White sandwich) estimators were used given departure from normality and large sample size [69, 70].”
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Metadata
- Authors
- Horton, N. J., & Lipsitz, S. R.
- Affiliations
- Harvard University (US)
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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