Módszerek összehasonlítása

	Bayes-féle Regresszió	Cox-féle proporcionális kockázati regresszió	Kaplan-Meier túlélésbecslő
Tudományterület	Bayes-statisztika	Túléléselemzés	Túléléselemzés
Módszercsalád	Bayesian methods	Survival analysis	Survival analysis
Keletkezés éve	—	1972	1958
Megalkotó	—	Cox, D. R.	Kaplan, E. L. & Meier, P.
Típus	Bayesian linear model	Semi-parametric hazard regression model	Non-parametric survival estimator
Alapmű	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.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Alternatív nevek	bayesian linear regression, probabilistic regression, bayesian regresyon	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Kapcsolódó	2	3	2
Összefoglaló	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.	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.