A comparison of multiplicity adjustment strategies for correlated binary endpoints

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.