方法对比

	实用生存分析	Kaplan-Meier分析
领域	流行病学	流行病学
方法族	Process / pipeline	Process / pipeline
起源年份	Conceptual framework: 1967; widespread application: 1990s–2000s	1958
提出者	Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward	Edward L. Kaplan and Paul Meier
类型	Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings	Nonparametric survival estimator
开创性文献	Ford, I., & Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375(5), 454–463.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
别名	real-world survival analysis, pragmatic time-to-event analysis, effectiveness survival analysis, PSA	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
相关	5	5
摘要	Pragmatic survival analysis applies time-to-event statistical methods within pragmatic or real-world settings, estimating how long patients survive, remain event-free, or retain treatment benefit under conditions of routine clinical practice. Unlike explanatory survival analyses conducted under tightly controlled trial conditions, the pragmatic variant embraces the heterogeneity, treatment switching, non-adherence, and competing events that characterise real-world patient populations, prioritising external validity over internal precision.	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.