So sánh phương pháp

	Hồi quy Poisson mạnh mẽ	Hồi quy Logistic
Lĩnh vực	Thống kê	Thống kê nghiên cứu
Họ	Regression model	Process / pipeline
Năm ra đời	2004	1958
Người khởi xướng	Guangyong Zou	David Roxbee Cox
Loại	GLM with robust variance	Method
Công trình gốc	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Tên gọi khác	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson	logit model, binomial logistic regression, LR
Liên quan	5	3
Tóm tắt	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.