Comparer des méthodes

	Régression de survie	Régression proportionnelle des risques de Cox	Estimateur de survie de Kaplan-Meier
Domaine	Statistique	Analyse de survie	Analyse de survie
Famille	Regression model	Survival analysis	Survival analysis
Année d'origine	1980s	1972	1958
Auteur d'origine	Kalbfleisch & Prentice; Cox & Oakes	Cox, D. R.	Kaplan, E. L. & Meier, P.
Type	Parametric survival model	Semi-parametric hazard regression model	Non-parametric survival estimator
Source fondatrice	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	accelerated failure time model, AFT model, parametric survival model, time-to-event regression	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Apparentées	3	3	2
Résumé	Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.