Módszerek összehasonlítása

	Bayes-i túlélésanalízis	Bayes-féle Regresszió	Cox-féle proporcionális kockázati regresszió
Tudományterület	Bayes-statisztika	Bayes-statisztika	Túléléselemzés
Módszercsalád	Bayesian methods	Bayesian methods	Survival analysis
Keletkezés éve	2001	—	1972
Megalkotó	Ibrahim, Chen & Sinha	—	Cox, D. R.
Típus	Bayesian time-to-event model	Bayesian linear model	Semi-parametric hazard regression model
Alapmű	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.
Alternatív nevek	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	bayesian linear regression, probabilistic regression, bayesian regresyon	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu
Kapcsolódó	4	2	3
Összefoglaló	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	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.