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

In: Robust and Multivariate Statistical Methods

Author

Listed:
  • 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)

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.

Suggested Citation

  • John Kolassa & Xinyan Chen & Yodit Seifu & Dewei Zhong, 2023. "Power Calculations and Critical Values for Two-Stage Nonparametric Testing Regimes," Springer Books, in: Mengxi Yi & Klaus Nordhausen (ed.), Robust and Multivariate Statistical Methods, pages 409-428, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-22687-8_19
    DOI: 10.1007/978-3-031-22687-8_19
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