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	Analisi Bayesiana di Kaplan-Meier	Analisi di sopravvivenza
Campo	Epidemiologia	Statistica per la ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1976	1958
Ideatore	Susarla & Van Ryzin (Bayesian nonparametric survival estimation)	Edward L. Kaplan and Paul Meier
Tipo	Bayesian nonparametric survival analysis	Method
Fonte seminale	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	Kaplan-Meier analysis, Cox regression, TTE analysis
Correlati	4	3
Sintesi	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.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.