Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Koksas proporcionālās bīstamības regresija× | Regresija izdzīvošanai× | |
|---|---|---|
| Nozare≠ | Dzīvildze | Statistika |
| Saime≠ | Survival analysis | Regression model |
| Izcelsmes gads≠ | 1972 | 1980s |
| Autors≠ | Cox, D. R. | Kalbfleisch & Prentice; Cox & Oakes |
| Tips≠ | Semi-parametric hazard regression model | Parametric survival model |
| Pirmavots≠ | 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. ISBN: 978-0471363576 |
| Citi nosaukumi | cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu | accelerated failure time model, AFT model, parametric survival model, time-to-event regression |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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. | Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood. |
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