Linganisha mbinu

	Regressioni ya Kuishi ya Weibull ya Parametric	Uchambuzi wa Nguvu kwa Ajili ya Tafiti za Usalama wa Maisha
Nyanja	Uchanganuzi wa Uhai	Takwimu
Familia	Survival analysis	Hypothesis test
Mwaka wa asili	1951	1981
Mwanzilishi	Waloddi Weibull	—
Aina	Fully parametric survival regression model	Sample size determination for survival outcomes
Chanzo asilia	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.	Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319.
Majina mbadala	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
Zinazohusiana	4	6
Muhtasari	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.