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| Bayes'sche Cox-Regression× | Survival Regression× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1972 (Cox PH); 2001 (Bayesian treatment) | 1980s |
| Urheber≠ | Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001) | Kalbfleisch & Prentice; Cox & Oakes |
| Typ≠ | Survival regression | Parametric survival model |
| Wegweisende Quelle≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 |
| Aliasnamen | Bayesian Cox PH model, Bayesian proportional hazards model, Bayesian survival regression, BCox | accelerated failure time model, AFT model, parametric survival model, time-to-event regression |
| Verwandt≠ | 6 | 3 |
| Zusammenfassung≠ | 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. | Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood. |
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