방법 비교

	위험 조정 생존 분석	성향 점수 매칭
분야	역학	연구 통계
계열	Process / pipeline	Process / pipeline
기원 연도	1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s	1983
창시자	D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others	Paul Rosenbaum and Donald Rubin
유형	Observational and experimental analytical method	Method
원전	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.	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.
별칭	covariate-adjusted survival analysis, adjusted time-to-event analysis, risk-stratified survival analysis, adjusted Kaplan-Meier / Cox analysis	PSM, propensity score weighting, covariate balance
관련	5	3
요약	Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazard ratios, survival curves, and median survival estimates that are attributable to the factor of interest rather than to pre-existing risk differences between groups.	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.