Recommendations on multiple testing adjustment in multi-arm trials with a shared control group. (May 2018)

• Record Type: Journal Article
• Authors: Howard, Dena R; Brown, Julia M; Todd, Susan; Gregory, Walter M
• Is Part Of: Statistical methods in medical research. Volume 27:Number 5(2018)
• Page Start: 1513
• Page End: 1530
• Publication Date: 2018-05
• Subjects: Multiple testing -- multi-arm clinical trial -- family-wise error rate -- type-I error -- multiplicity -- shared control group -- correlated comparisons -- alpha adjustment; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216664759
• Languages: English
• ISSNs: 0962-2802

Abstract

Multi-arm clinical trials assessing multiple experimental treatments against a shared control group can offer efficiency advantages over independent trials through assessing an increased number of hypotheses. Published opinion is divided on the requirement for multiple testing adjustment to control the family-wise type-I error rate (FWER). The probability of a false positive error in multi-arm trials compared to equivalent independent trials is affected by the correlation between comparisons due to sharing control data. We demonstrate that this correlation in fact leads to a reduction in the FWER, therefore FWER adjustment is not recommended solely due to sharing control data. In contrast, the correlation increases the probability of multiple false positive outcomes across the hypotheses, although standard FWER adjustment methods do not control for this. A stringent critical value adjustment is proposed to maintain equivalent evidence of superiority in two correlated comparisons to that obtained within independent trials. FWER adjustment is only required if there is an increased chance of making a single claim of effectiveness by testing multiple hypotheses, not due to sharing control data. For competing experimental therapies, the correlation between comparisons can be advantageous as it eliminates bias due to the experimental therapies being compared to different control populations.