Statistical power of multiplicity adjustment strategies for correlated binary endpoints

Andrew C. Leon, Moonseong Heo, Jedediah J. Teres, Toshihiko Morikawa

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


There are numerous alternatives to the so-called Bonferroni adjustment to control for familywise Type I error among multiple tests. Yet, for the most part, these approaches disregard the correlation among endpoints. This can prove to be a conservative hypothesis testing strategy if the null hypothesis is false. The James procedure was proposed to account for the correlation structure among multiple continuous endpoints. Here, a simulation study evaluates the statistical power of the Hochberg and James adjustment strategies relative to that of the Bonferroni approach when used for multiple correlated binary variables. The simulations demonstrate that relative to the Bonferroni approach, neither alternative sacrifices power. The Hochberg approach has more statistical power for ρ<0.50; whereas the James procedure provides more statistical power with higher ρ, the common correlation among the multiple outcomes. A study of gender differences in New York City homicides is used to illustrate the approaches.

Original languageEnglish (US)
Pages (from-to)1712-1723
Number of pages12
JournalStatistics in Medicine
Issue number8
StatePublished - Apr 15 2007
Externally publishedYes


  • Bonferroni adjustment
  • Correlated binary endpoints
  • James adjustment
  • Multiple comparisons
  • Multiplicity
  • Statistical power

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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