Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovská Coxova regrese× | Bayesovská regresní analýza přežití× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1972 (Cox PH); 2001 (Bayesian treatment) | 1990s–2001 |
| Tvůrce≠ | Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001) | Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al. |
| Typ≠ | Survival regression | Bayesian parametric/semiparametric regression |
| Původní zdroj | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 |
| Další názvy | Bayesian Cox PH model, Bayesian proportional hazards model, Bayesian survival regression, BCox | Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model |
| Příbuzné≠ | 6 | 5 |
| Shrnutí≠ | Bayesian Cox regression combines the Cox proportional hazards model for time-to-event data with Bayesian inference. Instead of point estimates, it produces full posterior distributions over the hazard ratios, naturally incorporating prior knowledge and providing coherent uncertainty quantification even with small samples or informative censoring. | 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. |
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