Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия выживаемости× | Вейбулловская параметрическая регрессия выживаемости× | |
|---|---|---|
| Область≠ | Статистика | Анализ выживаемости |
| Семейство≠ | Regression model | Survival analysis |
| Год появления≠ | 1980s | 1951 |
| Автор метода≠ | Kalbfleisch & Prentice; Cox & Oakes | Waloddi Weibull |
| Тип≠ | Parametric survival model | Fully parametric survival regression model |
| Основополагающий источник≠ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| Другие названия | accelerated failure time model, AFT model, parametric survival model, time-to-event regression | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| Связанные≠ | 3 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
|
|