方法对比
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| 贝叶斯生存分析× | 威布尔参数生存回归× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 生存分析 |
| 方法族≠ | Bayesian methods | Survival analysis |
| 起源年份≠ | 2001 | 1951 |
| 提出者≠ | Ibrahim, Chen & Sinha | Waloddi Weibull |
| 类型≠ | Bayesian time-to-event model | Fully parametric survival regression model |
| 开创性文献≠ | Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer. DOI ↗ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| 别名≠ | bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| 相关 | 4 | 4 |
| 摘要≠ | Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates. | Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival. |
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