Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia Parametrică de Supraviețuire Weibull× | Analiza Bayesiană a Supraviețuirii× | |
|---|---|---|
| Domeniu≠ | Supraviețuire | Bayesian |
| Familie≠ | Survival analysis | Bayesian methods |
| Anul apariției≠ | 1951 | 2001 |
| Autorul original≠ | Waloddi Weibull | Ibrahim, Chen & Sinha |
| Tip≠ | Fully parametric survival regression model | Bayesian time-to-event model |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative≠ | 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 |
| Înrudite | 4 | 4 |
| Rezumat≠ | 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. |
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