Sammenlign metoder

	Bayesiansk analyse af konkurrerende risici	Kaplan-Meier Estimator
Fagområde	Epidemiologi	Statistik
Familie	Process / pipeline	Survival analysis
Oprindelsesår	1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)	1958
Ophavsperson	Various; Bayesian formulation advanced by Gelfand, Dey, Larson, and Dinse among others	Edward L. Kaplan and Paul Meier
Type	Bayesian survival/time-to-event model	Nonparametric estimator
Oprindelig kilde	Larson, M. G., & Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics, 34(3), 201–211.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Aliasser	Bayesian cause-specific hazard model, Bayesian subdistribution hazard model, BCRA, Bayesian cumulative incidence analysis	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
Relaterede	3	2
Resumé	Bayesian competing risks analysis is a time-to-event method for settings where subjects can fail from more than one mutually exclusive cause — such as death from cancer versus death from cardiovascular disease — and prior knowledge or small-sample uncertainty makes a Bayesian framework advantageous. It extends classical competing risks models (cause-specific hazards and cumulative incidence functions) by placing probability distributions over unknown parameters and updating those distributions with observed data, yielding full posterior inference for each failure type.	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.