Sammenlign metoder

	Bayesiansk overlevelsesanalyse	Bayesiansk regression	Cox Proportional Hazards Regression
Fagområde	Bayesiansk	Bayesiansk	Overlevelsesanalyse
Familie	Bayesian methods	Bayesian methods	Survival analysis
Oprindelsesår	2001	—	1972
Ophavsperson	Ibrahim, Chen & Sinha	—	Cox, D. R.
Type	Bayesian time-to-event model	Bayesian linear model	Semi-parametric hazard regression model
Oprindelig kilde	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.
Aliasser	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
Relaterede	4	2	3
Resumé	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.