Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Uhai wa Bayesian× | Rega ya Hatari za Uwiano wa Cox× | |
|---|---|---|
| Nyanja≠ | Mbinu za Bayes | Uchanganuzi wa Uhai |
| Familia≠ | Bayesian methods | Survival analysis |
| Mwaka wa asili≠ | 2001 | 1972 |
| Mwanzilishi≠ | Ibrahim, Chen & Sinha | Cox, D. R. |
| Aina≠ | Bayesian time-to-event model | Semi-parametric hazard regression model |
| Chanzo asilia≠ | Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer. DOI ↗ | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ |
| Majina mbadala≠ | bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu |
| Zinazohusiana≠ | 4 | 3 |
| Muhtasari≠ | 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. | Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor. |
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