Sammenlign metoder

	Risikojustert analyse av konkurrerende risikoer	Kaplan-Meier-estimatoren
Fagfelt	Epidemiologi	Statistikk
Familie	Process / pipeline	Survival analysis
Opprinnelsesår	1999 (subdistribution hazard model); cause-specific hazard framework earlier	1958
Opphavsperson	Jason Fine and Robert Gray	Edward L. Kaplan and Paul Meier
Type	Regression model for time-to-event data with competing events	Nonparametric estimator
Opprinnelig kilde	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	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
Relaterte	4	2
Sammendrag	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.	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.