विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| कॉक्स प्रोपोर्शनल हैज़र्ड्स रिग्रेशन× | वेइबुल पैरामीट्रिक सर्वाइवल रिग्रेशन× | |
|---|---|---|
| क्षेत्र | उत्तरजीविता | उत्तरजीविता |
| परिवार | Survival analysis | Survival analysis |
| उद्भव वर्ष≠ | 1972 | 1951 |
| प्रवर्तक≠ | Cox, D. R. | Waloddi Weibull |
| प्रकार≠ | Semi-parametric hazard regression model | Fully parametric survival regression model |
| मौलिक स्रोत≠ | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| उपनाम | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| संबंधित≠ | 3 | 4 |
| सारांश≠ | 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. | 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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