Robust Poisson Regression
Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression.
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- Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706. · DOI 10.1093/aje/kwh090
- Zou, G. Y., & Donner, A. (2013). Extension of the modified Poisson regression model to prospective studies with binary data: why it is simpler than it sounds. Journal of Clinical Epidemiology, 66(9), 1023-1028. · URL
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