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Permutation-based multiple testing corrections for P $$ P $$ -values and confidence intervals for cluster randomized

Samuel I Watson1, Joshua O Akinyemi2, Karla Hemming1

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|June 21, 2023
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Summary
This summary is machine-generated.

This study presents methods for P-value corrections and confidence intervals in cluster randomized trials with multiple outcomes. The Romano-Wolf procedure offers better error control and efficiency for treatment effect estimation.

Keywords:
cluster randomized trialcoverageinferencemultiple testing

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Inference

Background:

  • Cluster randomized trials (CRTs) with multiple outcomes present challenges for statistical inference.
  • Existing methods for P-value correction and confidence interval construction are limited in this setting.

Purpose of the Study:

  • To derive and compare methods for P-value corrections and confidence intervals in CRTs with multiple outcomes.
  • To ensure strong control of family-wise error rates and coverage for treatment effect estimates.

Main Methods:

  • Adapted Bonferroni, Holm, and Romano-Wolf methods for CRT inference using permutation tests.
  • Developed a novel search procedure for confidence set limits via permutation tests.
  • Conducted simulation studies comparing error rates, coverage, and efficiency using model-based and permutation tests.

Main Results:

  • The Romano-Wolf type procedure demonstrated nominal error rates and coverage, even with non-independent correlations.
  • This procedure was more efficient than other methods in simulation studies.
  • Results were validated against a real-world trial analysis.

Conclusions:

  • The Romano-Wolf procedure is a robust and efficient method for statistical inference in CRTs with multiple outcomes.
  • The developed permutation-based approach enhances P-value correction and confidence interval construction in this complex setting.