Comparar métodos

	Análisis bayesiano de Kaplan-Meier	Análisis de Kaplan-Meier
Campo	Epidemiología	Epidemiología
Familia	Process / pipeline	Process / pipeline
Año de origen	1976	1958
Autor original	Susarla & Van Ryzin (Bayesian nonparametric survival estimation)	Edward L. Kaplan and Paul Meier
Tipo	Bayesian nonparametric survival analysis	Nonparametric survival estimator
Fuente seminal	Susarla, V., & Van Ryzin, J. (1976). Nonparametric Bayesian estimation of survival curves from incomplete observations. Journal of the American Statistical Association, 71(356), 897–902.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	Bayesian survival curve estimation, Bayesian nonparametric survival analysis, Dirichlet process Kaplan-Meier, BKM	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
Relacionados	4	5
Resumen	Bayesian Kaplan-Meier analysis extends the classical Kaplan-Meier estimator by placing a prior distribution over the survival function and updating it with observed time-to-event data to obtain a full posterior distribution for the survival curve. This approach, rooted in Susarla and Van Ryzin's 1976 Dirichlet-process framework, yields credible intervals rather than confidence intervals and enables coherent incorporation of prior clinical knowledge, making it particularly valuable in small-sample or early-phase clinical settings.	Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.