Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Регресия на пропорционалните опасности на Кокс× | Модел със споделена несигурност за клъстерни данни за преживяемост× | |
|---|---|---|
| Област | Анализ на преживяемостта | Анализ на преживяемостта |
| Семейство | Survival analysis | Survival analysis |
| Година на възникване≠ | 1972 | 1979 |
| Създател≠ | Cox, D. R. | Vaupel, J.W., Manton, K.G. & Stallard, E. |
| Тип≠ | Semi-parametric hazard regression model | Random effects survival model |
| Основополагащ източник≠ | Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ | Vaupel, J.W., Manton, K.G. & Stallard, E. (1979). The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography, 16(3), 439–454. DOI ↗ |
| Други названия≠ | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu | shared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık) |
| Свързани | 3 | 3 |
| Резюме≠ | 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. | The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When survival outcomes are measured on individuals who share a common environment (patients in the same hospital, members of the same family, animals in the same litter), a frailty term accounts for the within-cluster dependence that ordinary Cox regression ignores. |
| ScholarGateНабор от данни ↗ |
|
|