Confronta i metodi

	Analisi delle cause competitive con abbinamento	Stimatore di Kaplan-Meier
Campo	Epidemiologia	Statistica
Famiglia	Process / pipeline	Survival analysis
Anno di origine	1999 (Fine-Gray model); extended to matched designs ~2010s	1958
Ideatore	Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)	Edward L. Kaplan and Paul Meier
Tipo	Observational survival analysis with matching and competing events	Nonparametric estimator
Fonte seminale	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	matched Fine-Gray analysis, propensity-matched competing risks, matched cause-specific hazard analysis, matched subdistribution hazard analysis	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
Correlati	4	2
Sintesi	Matched competing risks analysis combines subject-level matching (e.g., propensity-score matching) with competing risks survival methods to estimate the cause-specific or subdistribution hazard of an event of interest while accounting for competing events that preclude the occurrence of that event. It is widely used in clinical and epidemiological observational studies where patients may die from causes other than the primary outcome of interest, and where treatment groups differ on baseline confounders.	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.