The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
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].”
Citations
Cites
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Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Longitudinal Data Analysis for Discrete and Continuous Outcomes
Zeger, S. L., & Liang, K. (1986) · Biometrics · doi:10.2307/2531248 · Cited by 7,781
Everitt, B. S. (2001) · Psychology Press eBooks · doi:10.4324/9781410605542-11 · Cited by 6,877
(1967) · Project Euclid (Cornell University) · Cited by 4,720
Maximum Likelihood Estimation of Misspecified Models
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Research works printed but not linked: 4
- 10.1080/00031305.1994.10476073 · DOI
- 1996, Splus Version 3.4, Supplement
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- 1997, Stata Statistical Software: Release 5.0
Cited by
Sorted by citations
Items 101–103 of 103
McKenzie, J. E., O'Connor, D. A., Page, M. J., Mortimer, D. S., French, S. D., Walker, B. F., Keating, J. L., Grimshaw, J. M., Michie, S., Francis, J. J., et al. (2010) · Implementation Science · doi:10.1186/1748-5908-5-86 · Cited by 42
Prepregnancy Depressive Symptoms and Preterm Birth in the Black Women's Health Study
Phillips, G. S., Wise, L. A., Rich-Edwards, J. W., Stampfer, M. J., & Rosenberg, L. (2010) · Annals of Epidemiology · doi:10.1016/j.annepidem.2009.09.009 · Cited by 33
Smithman, M. A., Haggerty, J., Gaboury, I., & Breton, M. (2022) · BMC Primary Care · doi:10.1186/s12875-022-01850-4 · Cited by 25
“Robust (Huber-White sandwich) estimators were used given departure from normality and large sample size [69, 70].”
Method
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
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