A comparison of multiplicity adjustment strategies for correlated binary endpoints

Andrew C. Leon, Moonseong Heo

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Several Bonferroni-type adjustments have been proposed to control for familywise type I error among multiple tests. However, many of the approaches disregard the correlation among endpoints. This can result in a conservative hypothesis testing strategy. The James procedure is an alternative approach that accounts for multiplicity among correlated continuous endpoints. Here a simulation study compares four Bonferroni-type alpha-adjustments (Bonferroni, Dunn-Sidak, Holm, and Hochberg) and the James p-value adjustment when used for multiple correlated binary variables. These procedures provided adequate protection against familywise type 1 error for correlated binary endpoints, albeit, at times, in an overly cautious manner. That is, when correlations among endpoints exceed 0.60, the result is somewhat conservative for the approaches that do not account for those correlations. Among the adjustments examined, the James approach appears to be the uniformly preferred method. Analyses of data from a randomized controlled clinical trial of treatments for mania in bipolar disorder are used to illustrate the application of the multiplicity adjustments.

Original languageEnglish (US)
Pages (from-to)839-855
Number of pages17
JournalJournal of Biopharmaceutical Statistics
Volume15
Issue number5
DOIs
StatePublished - 2005
Externally publishedYes

Fingerprint

Social Adjustment
Multiplicity
Adjustment
Bonferroni
Binary
Bipolar Disorder
Multiple Tests
Binary Variables
Type I error
p-Value
Hypothesis Testing
Clinical Trials
Disorder
Exceed
Randomized Controlled Trials
Strategy
Simulation Study
Alternatives

Keywords

  • Bonferroni adjustment
  • Correlated binary endpoints
  • Multiplicity
  • Type I error

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

A comparison of multiplicity adjustment strategies for correlated binary endpoints. / Leon, Andrew C.; Heo, Moonseong.

In: Journal of Biopharmaceutical Statistics, Vol. 15, No. 5, 2005, p. 839-855.

Research output: Contribution to journalArticle

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