Võrdle meetodeid

	Fine-Gray konkureerivate riskide mudel	Kaplan-Meieri elulemuse estimaator
Valdkond	Statistika	Elukestusanalüüs
Perekond	Hypothesis test	Survival analysis
Tekkeaasta	1999	1958
Looja	Jason P. Fine & Robert J. Gray	Kaplan, E. L. & Meier, P.
Tüüp	Subdistribution hazard regression	Non-parametric survival estimator
Algallikas	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.
Rööpnimetused	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Seotud	5	2
Kokkuvõte	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.