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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.