Comparer des méthodes

	Analyse de puissance pour les études de survie	Analyse de puissance pour le test t
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1981	1969
Auteur d'origine	—	Jacob Cohen
Type	Sample size determination for survival outcomes	Sample size determination
Source fondatrice	Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
Alias	log-rank power analysis, cox regression power analysis, survival power analysis, Sağkalım Analizi Güç Analizi	t-test power analysis, sample size calculation for t-test, Güç Analizi — t-Testi
Apparentées	6	5
Résumé	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.	Power analysis for the t-test is a sample size planning procedure that determines how many participants are required to detect a mean difference of a given magnitude with acceptable probability. Formalised by Jacob Cohen in his 1969 and 1988 editions of Statistical Power Analysis for the Behavioral Sciences, it links four quantities — effect size (Cohen's d), significance level (α), statistical power (1 − β), and sample size — so that fixing any three allows calculation of the fourth.