Comparer des méthodes

	Analyse des risques concurrents ajustés aux risques	Analyse de survie
Domaine	Épidémiologie	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999 (subdistribution hazard model); cause-specific hazard framework earlier	1958
Auteur d'origine	Jason Fine and Robert Gray	Edward L. Kaplan and Paul Meier
Type	Regression model for time-to-event data with competing events	Method
Source fondatrice	Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	competing risks regression, subdistribution hazard model, cause-specific hazard analysis, Fine-Gray model	Kaplan-Meier analysis, Cox regression, TTE analysis
Apparentées	4	3
Résumé	Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confounders, the method yields unbiased estimates of the absolute probability — the cumulative incidence function — of each event type over time in the presence of competing events.	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.