Methoden vergleichen

	Bayesian Survival Analysis	Cox-Proportional-Hazards-Regression	Weibull Parametrische Überlebensregression
Fachgebiet	Bayes-Statistik	Überlebenszeitanalyse	Überlebenszeitanalyse
Familie	Bayesian methods	Survival analysis	Survival analysis
Entstehungsjahr	2001	1972	1951
Urheber	Ibrahim, Chen & Sinha	Cox, D. R.	Waloddi Weibull
Typ	Bayesian time-to-event model	Semi-parametric hazard regression model	Fully parametric survival regression model
Wegweisende Quelle	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.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Aliasnamen	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Verwandt	4	3	4
Zusammenfassung	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.	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.