So sánh phương pháp

	Nghiên cứu trường hợp-chứng có điều chỉnh rủi ro	Ghép cặp điểm xu hướng
Lĩnh vực	Dịch tễ học	Thống kê nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s)	1983
Người khởi xướng	Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980)	Paul Rosenbaum and Donald Rubin
Loại	Observational analytic study design	Method
Công trình gốc	Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195029697	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Tên gọi khác	adjusted case-control study, covariate-adjusted case-control, risk-stratified case-control study, matched and adjusted case-control study	PSM, propensity score weighting, covariate balance
Liên quan	5	3
Tóm tắt	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.