विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बायेसियन सर्वाइवल रिग्रेशन× | कॉक्स प्रोपोर्शनल हैज़र्ड्स रिग्रेशन× | |
|---|---|---|
| क्षेत्र≠ | सांख्यिकी | उत्तरजीविता |
| परिवार≠ | Regression model | Survival analysis |
| उद्भव वर्ष≠ | 1990s–2001 | 1972 |
| प्रवर्तक≠ | Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al. | Cox, D. R. |
| प्रकार≠ | Bayesian parametric/semiparametric regression | Semi-parametric hazard regression model |
| मौलिक स्रोत≠ | Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772 | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ |
| उपनाम | Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | Bayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects. | 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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