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| Bayes'sche Überlebensregression× | Cox-Proportional-Hazards-Regression× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Überlebenszeitanalyse |
| Familie≠ | Regression model | Survival analysis |
| Entstehungsjahr≠ | 1990s–2001 | 1972 |
| Urheber≠ | Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al. | Cox, D. R. |
| Typ≠ | Bayesian parametric/semiparametric regression | Semi-parametric hazard regression model |
| Wegweisende Quelle≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ |
| Aliasnamen | Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | Bayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects. | 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. |
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