Linganisha mbinu

	Uchanganuzi wa Uhai wa Bayesian	Kikokotozi cha Kuishi cha Kaplan-Meier
Nyanja	Mbinu za Bayes	Uchanganuzi wa Uhai
Familia	Bayesian methods	Survival analysis
Mwaka wa asili	2001	1958
Mwanzilishi	Ibrahim, Chen & Sinha	Kaplan, E. L. & Meier, P.
Aina	Bayesian time-to-event model	Non-parametric survival estimator
Chanzo asilia	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.
Majina mbadala	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Zinazohusiana	4	2
Muhtasari	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.