Power Calculations and Critical Values for Two-Stage Nonparametric Testing Regimes

John Kolassa (), Xinyan Chen (), Yodit Seifu and Dewei Zhong ()
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John Kolassa: Rutgers, The State University of New Jersey
Xinyan Chen: Rutgers, The State University of New Jersey
Yodit Seifu: Anheuser-Busch Companies, LLC
Dewei Zhong: Bristol-Myers Squibb

A chapter in Robust and Multivariate Statistical Methods, 2023, pp 409-428 from Springer

Abstract: Abstract Interim analysis techniques for clinical trials provide improved power with smaller average sample sizes. These techniques crucially require multivariate probability calculations for determining critical values. Most existing techniques rely on multivariate normal approximations to the joint null distribution of test statistics evaluated on potential interim and full data sets. More accurate critical values for nonparametric testing with an interim analysis are given, using a new multivariate Cornish–Fisher expansion. While earlier authors demonstrated that such an expansion is possible, it has never been implemented before this manuscript. Generally, the superior accuracy of power calculations via an Edgeworth series is demonstrated. Example calculations giving sample sizes from desired power are provided. Calculations are implemented in an R package.

Keywords: Cornish–Fisher expansion; Multi-stage testing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-22687-8_19

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