Порівняння методів

	Байєсівський аналіз виживаності	Оцінювач виживаності Каплана-Мейєра	Регресія Вейбулла (Weibull regression)
Галузь	Баєсові методи	Аналіз виживаності	Аналіз виживаності
Родина	Bayesian methods	Survival analysis	Survival analysis
Рік появи	2001	1958	1951
Автор методу	Ibrahim, Chen & Sinha	Kaplan, E. L. & Meier, P.	Waloddi Weibull
Тип	Bayesian time-to-event model	Non-parametric survival estimator	Fully parametric survival regression model
Основоположне джерело	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.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Інші назви	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	product-limit estimator, km curve, kaplan-meier sağkalım analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Пов'язані	4	2	4
Підсумок	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.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.