方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 威布尔参数生存回归× | 贝叶斯生存分析× | |
|---|---|---|
| 领域≠ | 生存分析 | 贝叶斯 |
| 方法族≠ | Survival analysis | Bayesian methods |
| 起源年份≠ | 1951 | 2001 |
| 提出者≠ | Waloddi Weibull | Ibrahim, Chen & Sinha |
| 类型≠ | Fully parametric survival regression model | Bayesian time-to-event model |
| 开创性文献≠ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ | Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer. DOI ↗ |
| 别名≠ | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma | bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model |
| 相关 | 4 | 4 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
|
|