方法对比

	风险调整病例对照研究	逻辑回归
领域	流行病学	研究统计学
方法族	Process / pipeline	Process / pipeline
起源年份	1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s)	1958
提出者	Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980)	David Roxbee Cox
类型	Observational analytic study design	Method
开创性文献	Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195029697	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
别名	adjusted case-control study, covariate-adjusted case-control, risk-stratified case-control study, matched and adjusted case-control study	logit model, binomial logistic regression, LR
相关	5	3
摘要	A risk-adjusted case-control study is an observational design that identifies individuals with a disease outcome (cases) and comparable individuals without it (controls), then uses statistical adjustment — most commonly multivariable logistic regression — to estimate the association between an exposure and the outcome while controlling for confounding risk factors. The adjustment step is what distinguishes this variant from a simple case-control study, producing odds ratios that better reflect the independent contribution of the exposure of interest.	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.