Comparer des méthodes

	Modèle à risques concurrents de Fine-Gray	Estimateur de survie de Kaplan-Meier
Domaine	Statistique	Analyse de survie
Famille	Hypothesis test	Survival analysis
Année d'origine	1999	1958
Auteur d'origine	Jason P. Fine & Robert J. Gray	Kaplan, E. L. & Meier, P.
Type	Subdistribution hazard regression	Non-parametric survival estimator
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, Fine-Gray model, Fine-Gray Competing Risks Modeli	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Apparentées	5	2
Résumé	The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities.	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.