Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovská analýza přežití× | Weibullova parametrická regresní analýza přežití× | |
|---|---|---|
| Obor≠ | Bayesovská statistika | Analýza přežití |
| Rodina≠ | Bayesian methods | Survival analysis |
| Rok vzniku≠ | 2001 | 1951 |
| Tvůrce≠ | Ibrahim, Chen & Sinha | Waloddi Weibull |
| Typ≠ | Bayesian time-to-event model | Fully parametric survival regression model |
| Původní zdroj≠ | 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 ↗ |
| Další názvy≠ | 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 |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | 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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