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P-values after repeated significance testing: a simple approximation method

Y J Lee1, H Quan

  • 1Biometry and Mathematical Statistics Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892.

Statistics in Medicine
|April 15, 1993
PubMed
Summary
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Calculating P-values for repeated significance tests is complex. This study introduces a simple approximation method, providing tables for common procedures to improve efficiency and accuracy in statistical analysis.

Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Repeated significance testing is crucial in clinical trials for interim analyses.
  • Traditional P-value computation is numerically intensive and conceptually complex due to sample space ordering.
  • Existing methods for ordering sample spaces have limitations in providing reasonable confidence intervals or P-values.

Purpose of the Study:

  • To develop a simplified method for approximating P-values in repeated significance testing.
  • To provide practical tools (tables) for implementing the P-value approximation.
  • To ensure the method's applicability across different sample space orderings and common statistical procedures.

Main Methods:

  • Investigated two distinct sample space orderings: Tsiatis et al. and Rosner and Tsiatis/Chang.

Related Experiment Videos

  • Developed a novel, computer-efficient approximation method for P-values.
  • Generated tables for implementing the approximation for 2-10 stages with common alpha levels (0.1, 0.05, 0.01) for Pocock and O'Brien-Fleming procedures.

    • Main Results:

    • The proposed method offers a computationally simpler alternative to numerical integration for P-value calculation.
    • The approximation method is applicable to both investigated sample space orderings.
    • Tables are provided for practical application, enhancing the usability of repeated significance testing.

    Conclusions:

    • A straightforward P-value approximation method has been successfully developed and validated.
    • The method simplifies the complex computation of P-values in repeated significance testing.
    • The provided tables facilitate the application of this approximation, improving efficiency in statistical analysis.