방법 비교

	매칭된 Kaplan-Meier 분석	생존 분석
분야	역학	연구 통계
계열	Process / pipeline	Process / pipeline
기원 연도	1958 (KM); matched application formalized 1980s–2000s	1958
창시자	Kaplan & Meier (KM method, 1958); matching extensions developed through propensity score methods (Rosenbaum & Rubin, 1983)	Edward L. Kaplan and Paul Meier
유형	Nonparametric survival analysis with observational confounder control	Method
원전	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
별칭	KM analysis in matched cohorts, propensity-matched survival curves, matched survival analysis, paired Kaplan-Meier	Kaplan-Meier analysis, Cox regression, TTE analysis
관련	6	3
요약	Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling for confounders that would otherwise distort treatment or exposure comparisons in observational data.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.