title: Evaluation of the statistical power for multiple tests: a case study.
abstract: It is challenging to estimate the statistical power when a complicated testing strategy is used to adjust for the type-I error for multiple comparisons in a clinical trial. In this paper, we use the Bonferroni Inequality to estimate the lower bound of the statistical power assuming that test statistics are approximately normally distributed and the correlation structure among test statistics is unknown or only partially known. The method was applied to the design of a clinical study for sample size and statistical power estimation.
authors.affiliation: Eli Lilly and Company, Indianapolis, IN 48285, USA. yeo adeline ai lin@lilly.com
authors.forename: Adeline
authors.initials: A
authors.lastname: Yeo
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size