방법 비교

	베이지안 생존 분석	Kaplan-Meier 생존 추정량
분야	베이지안	생존분석
계열	Bayesian methods	Survival analysis
기원 연도	2001	1958
창시자	Ibrahim, Chen & Sinha	Kaplan, E. L. & Meier, P.
유형	Bayesian time-to-event model	Non-parametric survival estimator
원전	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.
별칭	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	product-limit estimator, km curve, kaplan-meier sağkalım analizi
관련	4	2
요약	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.