方法对比
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| 贝叶斯Cox比例风险模型× | 贝叶斯随机对照试验× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1972 (Cox); Bayesian formulation developed through the 1990s | 1980s–2000s (formal methodology consolidated ~2004–2006) |
| 提出者≠ | 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) |
| 类型≠ | Bayesian semiparametric survival regression | Randomized experimental study with Bayesian inference |
| 开创性文献≠ | 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 |
| 别名 | Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model | Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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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