方法对比
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| 贝叶斯 Cox 回归× | 贝叶斯混合效应模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1972 (Cox PH); 2001 (Bayesian treatment) | 1990s–2000s (modern Bayesian MCMC era) |
| 提出者≠ | Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001) | Gelman, Hill, and the broader Bayesian hierarchical modeling tradition |
| 类型≠ | Survival regression | Bayesian regression model |
| 开创性文献≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| 别名 | Bayesian Cox PH model, Bayesian proportional hazards model, Bayesian survival regression, BCox | Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously. |
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