Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix
Abstract
Summary: Background: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure. Objectives: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE. Methods: We introduce the GEE in a formal way, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables. Results: Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient. Conclusions: The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.
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When the number of experimental units was small, the GEE1 sandwich estimator of the variance provided standard errors that were too small and therefore inflated the type 1 error rate.
“When the number of experimental units was small, the GEE1 sandwich estimator of the variance provided standard errors that were too small and therefore inflated the type 1 error rate31,32.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [31]
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- Authors
- Vens, M., & Ziegler, A.
- Affiliations
- University Hospital Schleswig-Holstein (DE)
- ORCID
- Ziegler, A.
- Venue
- Methods of Information in Medicine
- Publisher
- Georg Thieme Verlag KG
- Published
- 2010
- Volume
- 49
- Issue
- 05
- Pages
- 421-425
- ISSN
- 0026-1270 (ISSN-L)
- 2511-705X
- OpenAlex
- W4292366122
- Cited by
- 127
- References
- 0
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 13 · 2023: 13 · 2022: 8 · 2021: 12 · 2020: 7 · 2019: 5 · 2018: 8 · 2017: 10 · 2016: 6 · 2015: 5 · 2014: 8 · 2013: 5 · 2012: 1
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Methods of Information in Medicine (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- generalized estimating equations, working correlation matrix, dichotomous dependent variable, correlated binary data, estimator efficiency, generalized linear models
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.
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