विधियों की तुलना करें

	वेइबुल पैरामीट्रिक सर्वाइवल रिग्रेशन	उत्तरजीविता अध्ययनों के लिए शक्ति विश्लेषण
क्षेत्र	उत्तरजीविता	सांख्यिकी
परिवार	Survival analysis	Hypothesis test
उद्भव वर्ष	1951	1981
प्रवर्तक	Waloddi Weibull	—
प्रकार	Fully parametric survival regression model	Sample size determination for survival outcomes
मौलिक स्रोत	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.
उपनाम	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
संबंधित	4	6
सारांश	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.