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| Bayesian Cox-Proportional-Hazards× | Bayesianer Randomized Clinical Trial (Bayesianer RCT)× | |
|---|---|---|
| Fachgebiet | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1972 (Cox); Bayesian formulation developed through the 1990s | 1980s–2000s (formal methodology consolidated ~2004–2006) |
| Urheber≠ | D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001) | Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design) |
| Typ≠ | Bayesian semiparametric survival regression | Randomized experimental study with Bayesian inference |
| Wegweisende Quelle≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756 |
| Aliasnamen | Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model | Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | The Bayesian Cox proportional hazards model combines Cox's classical semiparametric survival regression with Bayesian inference, replacing point estimates and p-values with full posterior distributions over regression coefficients. It handles right-censored time-to-event outcomes, quantifies uncertainty about hazard ratios in probabilistic terms, and allows the incorporation of prior clinical or historical knowledge directly into the analysis. | A Bayesian randomized clinical trial (Bayesian RCT) combines the rigour of random treatment allocation with Bayesian statistical inference, allowing researchers to incorporate prior evidence and update beliefs continuously as trial data accumulate. Unlike the classical frequentist RCT, it yields direct probability statements about treatment effects and supports pre-specified adaptive stopping rules based on posterior probabilities. |
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