방법 비교

	실용적 Kaplan-Meier 분석	Kaplan-Meier 분석
분야	역학	역학
계열	Process / pipeline	Process / pipeline
기원 연도	1958 (estimator); pragmatic application formalized 1967 onward	1958
창시자	Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967)	Edward L. Kaplan and Paul Meier
유형	Non-parametric survival estimator within pragmatic study design	Nonparametric survival estimator
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	pragmatic KM analysis, real-world Kaplan-Meier, pragmatic survival curve estimation, KM analysis in pragmatic trials	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
관련	5	5
요약	Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a treatment effect under ideal conditions, pragmatic designs accept real-world heterogeneity, and the resulting survival curves reflect the effectiveness of an intervention as it actually performs in clinical practice.	Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.