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Related Experiment Videos

A permutational step-up method of testing multiple outcomes

J F Troendle1

  • 1National Institute of Child Health and Human Development, Division of Epidemiology, Statistics, and Prevention Reserach, Bethesda, Maryland 20892, USA.

Biometrics
|September 1, 1996
PubMed
Summary
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This study introduces a new permutation method for adjusting p-values in multiple comparisons, offering a flexible alternative for analyzing related hypotheses in two-group comparisons.

Area of Science:

  • Statistics
  • Biostatistics
  • Multiple Comparisons

Background:

  • Adjusting p-values is crucial for multiple related hypotheses to control Type I errors.
  • Existing methods like analytic step-up procedures often rely on specific distributional or correlation assumptions.

Purpose of the Study:

  • To present a novel permutational step-up multiple comparison procedure.
  • To offer a data-driven alternative that bypasses restrictive statistical assumptions.

Main Methods:

  • A permutational step-up procedure is described for adjusting p-values.
  • The method is applied to scenarios comparing two groups across k related outcomes.
  • It conditions on observed data, avoiding assumptions about distributions or correlations.

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Main Results:

  • The proposed method provides an adjustment for k related hypotheses.
  • It is particularly useful when comparing two groups on multiple outcomes.
  • The procedure asymptotically controls the familywise probability of a Type I error.

Conclusions:

  • The permutational step-up method is a robust alternative to analytic procedures.
  • It offers valid statistical inference without requiring specific distribution or correlation structures.
  • This approach enhances the reliability of multiple testing in statistical analysis.