Comparer des méthodes

	Régression des risques proportionnels de Cox rétrospective	Analyse de Kaplan-Meier
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	1972	1958
Auteur d'origine	David R. Cox	Edward L. Kaplan and Paul Meier
Type	Semi-parametric survival regression	Nonparametric survival estimator
Source fondatrice	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	Cox PH regression (retrospective), retrospective Cox survival model, retrospective hazard regression, Cox model on historical data	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
Apparentées	5	5
Résumé	Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline hazard, making it the dominant analytic tool when the investigator works backward from already-recorded outcomes and exposures.	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.