Compară metode

	Analiza Bayesiană a Supraviețuirii	Estimatorul de Supraviețuire Kaplan-Meier
Domeniu	Bayesian	Supraviețuire
Familie	Bayesian methods	Survival analysis
Anul apariției	2001	1958
Autorul original	Ibrahim, Chen & Sinha	Kaplan, E. L. & Meier, P.
Tip	Bayesian time-to-event model	Non-parametric survival estimator
Sursa seminală	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Denumiri alternative	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Înrudite	4	2
Rezumat	Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.