Porovnať metódy

	Bayesovská analýza prežitia	Regresia proporcionálnych rizik Cox	Kaplanov-Meierov odhad prežitia
Odbor	Bayesovské metódy	Analýza prežívania	Analýza prežívania
Rodina	Bayesian methods	Survival analysis	Survival analysis
Rok vzniku	2001	1972	1958
Tvorca	Ibrahim, Chen & Sinha	Cox, D. R.	Kaplan, E. L. & Meier, P.
Typ	Bayesian time-to-event model	Semi-parametric hazard regression model	Non-parametric survival estimator
Pôvodný zdroj	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Ďalšie názvy	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Príbuzné	4	3	2
Zhrnutie	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.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	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.