Сравнение методов

	Вейбулловская параметрическая регрессия выживаемости	Анализ мощности для исследований выживаемости
Область	Анализ выживаемости	Статистика
Семейство	Survival analysis	Hypothesis test
Год появления	1951	1981
Автор метода	Waloddi Weibull	—
Тип	Fully parametric survival regression model	Sample size determination for survival outcomes
Основополагающий источник	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.
Другие 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
Связанные	4	6
Сводка	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.