Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Регресия на Вайбул за оцеляване (Weibull Parametric Survival Regression)× | Анализ на мощността за проучвания на преживяемостта× | |
|---|---|---|
| Област≠ | Анализ на преживяемостта | Статистика |
| Семейство≠ | Survival analysis | Hypothesis test |
| Година на възникване≠ | 1951 | 1981 |
| Създател≠ | Waloddi Weibull | — |
| Тип≠ | Fully parametric survival regression model | Sample size determination for survival outcomes |
| Основополагащ източник≠ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ | Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319. DOI ↗ |
| Други названия | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma | log-rank power analysis, cox regression power analysis, survival power analysis, Sağkalım Analizi Güç Analizi |
| Свързани≠ | 4 | 6 |
| Резюме≠ | 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. | Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981) and Lachin (1981) and remain the standard approach in clinical trial planning. |
| ScholarGateНабор от данни ↗ |
|
|