Comparer des méthodes

	Estimateur de Kaplan-Meier	Cox proportional hazards
Domaine	Statistique	Épidémiologie
Famille	Survival analysis	Process / pipeline
Année d'origine	1958	1972
Auteur d'origine	Edward L. Kaplan and Paul Meier	Sir David Roxbee Cox
Type	Nonparametric estimator	Semi-parametric regression model
Source fondatrice	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Alias	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator	Cox regression, Cox PH model, proportional hazards model, CPH
Apparentées	2	5
Résumé	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.