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| Regresja przeżycia× | Regresja proporcjonalnego hazardu Coxa× | Parametryczny model regresji przeżycia Weibulla× | |
|---|---|---|---|
| Dziedzina≠ | Statystyka | Analiza przeżycia | Analiza przeżycia |
| Rodzina≠ | Regression model | Survival analysis | Survival analysis |
| Rok powstania≠ | 1980s | 1972 | 1951 |
| Twórca≠ | Kalbfleisch & Prentice; Cox & Oakes | Cox, D. R. | Waloddi Weibull |
| Typ≠ | Parametric survival model | Semi-parametric hazard regression model | Fully parametric survival regression model |
| Źródło pierwotne≠ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 | 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 ↗ |
| Inne nazwy | accelerated failure time model, AFT model, parametric survival model, time-to-event regression | 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 |
| Pokrewne≠ | 3 | 3 | 4 |
| Podsumowanie≠ | 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. | 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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