Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia Parametrică de Supraviețuire Weibull× | Analiza puterii statistice pentru studii de supraviețuire× | |
|---|---|---|
| Domeniu≠ | Supraviețuire | Statistică |
| Familie≠ | Survival analysis | Hypothesis test |
| Anul apariției≠ | 1951 | 1981 |
| Autorul original≠ | Waloddi Weibull | — |
| Tip≠ | Fully parametric survival regression model | Sample size determination for survival outcomes |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | 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 |
| Înrudite≠ | 4 | 6 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
|
|