Performance of the Modified Poisson Regression Approach for Estimating Relative Risks From Clustered Prospective Data
Abstract
Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.
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The modified Poisson regression model provides estimates of relative risk rather than odds ratios.
“We chose the modified Poisson regression model over the logistic regression approach due to its ability to provide estimates of relative risk rather than ORs.42”
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“This study differs from a formal intervention trial aiming at assessing a biological effect or efficacy outcome, in which the intervention is tightly prespecified and protocol deviations result in exclusion of participants.”
- Authors
- Yelland, L. N., Salter, A. B., & Ryan, P.
- Affiliations
- The University of Adelaide (AU)
- Corresponding author
- Yelland, L. N.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-08-12
- Volume
- 174
- Issue
- 8
- Pages
- 984-992
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2145125317
- Cited by
- 470
- References
- 1
- Citations by year
- 2026: 50 · 2025: 62 · 2024: 44 · 2023: 38 · 2022: 46 · 2021: 36 · 2020: 33 · 2019: 34 · 2018: 26 · 2017: 34 · 2016: 23 · 2015: 21 · 2014: 12 · 2013: 8 · 2012: 2
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- modified Poisson regression, log-binomial regression, relative risk estimation, generalized estimating equations, robust variance estimation, convergence problems, type I error, bias, coverage, intervention research, observational studies
- MeSH terms
- Computer Simulation, Humans, Prospective Studies, Regression Analysis, Risk, Epidemiologic Research Design, Cluster Analysis, Poisson Distribution, Observation
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