Comparer des méthodes

	Régression des risques proportionnels de Cox rétrospective	Analyse de survie
Domaine	Épidémiologie	Statistiques de recherche
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	Method
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	Kaplan-Meier analysis, Cox regression, TTE analysis
Apparentées	5	3
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.	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.