Comparer des méthodes

	Analyse de Kaplan-Meier	Analyse de survie
Domaine	Épidémiologie	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1958	1958
Auteur d'origine	Edward L. Kaplan and Paul Meier	Edward L. Kaplan and Paul Meier
Type	Nonparametric survival estimator	Method
Source fondatrice	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.
Alias	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve	Kaplan-Meier analysis, Cox regression, TTE analysis
Apparentées	5	3
Résumé	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.	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.