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Multiple comparisons between two groups on multiple Bernoulli outcomes while accounting for covariates.

James F Troendle1

  • 1Biometry and Mathematical Statistics Branch, Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, National Institutes of Health, DHHS, Bethesda, MD 20892, USA. jt3t@nih.gov

Statistics in Medicine
|June 25, 2005
PubMed
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This study introduces a novel permutation test for analyzing multiple Bernoulli outcomes adjusted for covariates. The proposed method effectively controls the familywise error rate and demonstrates superior performance over vector bootstrap approaches.

Area of Science:

  • Biostatistics
  • Statistical Methods
  • Epidemiology

Background:

  • Adjusting for multiplicity with multiple outcome variables is complex.
  • Analyzing Bernoulli outcomes adjusted for covariates presents unique challenges.

Purpose of the Study:

  • To develop and evaluate a permutation test for multiple, covariate-adjusted Bernoulli outcomes.
  • To compare the proposed method with a vector bootstrap approach.

Main Methods:

  • Utilized step-down permutation tests adapted for stratified data.
  • Permuted outcome vectors within strata defined by discrete covariates.
  • Compared performance via simulation against a vector bootstrap method.

Main Results:

Related Experiment Videos

  • The permutation test effectively controls the familywise error rate at prespecified levels.
  • The proposed method demonstrated superior error control and statistical power compared to the vector bootstrap approach.
  • The method was successfully illustrated on a dataset of infant malformations.

    • Conclusions:

    • Permutation tests within strata offer a robust solution for multiplicity adjustment with covariate-adjusted Bernoulli outcomes.
    • This approach provides improved error control and power for complex statistical analyses.
    • The findings have implications for epidemiological studies involving binary outcomes.