Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Veibula parametriskā izdzīvošanas regresija× | Jaudas analīze izdzīvošanas pētījumiem× | |
|---|---|---|
| Nozare≠ | Dzīvildze | Statistika |
| Saime≠ | Survival analysis | Hypothesis test |
| Izcelsmes gads≠ | 1951 | 1981 |
| Autors≠ | Waloddi Weibull | — |
| Tips≠ | Fully parametric survival regression model | Sample size determination for survival outcomes |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | 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 |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | 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. |
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