Adaptive non-inferiority margins under observable non-constancy. (November 2019)

• Record Type: Journal Article
• Authors: Hanscom, Brett; Hughes, James P; Williamson, Brian D; Donnell, Deborah
• Is Part Of: Statistical methods in medical research. Volume 28:Number 10/11(2019)
• Page Start: 3318
• Page End: 3332
• Publication Date: 2019-11
• Subjects: Non-inferiority margin -- adaptive design -- non-constancy -- meta-analysis -- HIV prevention -- PrEP; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218801134
• Languages: English
• ISSNs: 0962-2802

Abstract

A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the 'constancy' assumption. If the constancy assumption fails, decisions based on the chosen non-inferiority margin may be incorrect, and the study runs the risk of approving an inferior product or failing to approve a beneficial product. The constancy assumption cannot be validated in a trial without a placebo arm, and it is unlikely ever to be met completely. When there are strong, observable predictors of constancy, such as dosing and adherence to the active-control product, we can specify conditions where the constancy assumption will likely fail. We propose a method for using measurable predictors of active-control effectiveness to specify non-inferiority margins targeted to the planned study population characteristics. We describe a pre-specified method, using baseline characteristics or post-baseline predictors in the active-control arm, to adapt the non-inferiority margin at the end of the study if constancy is violated. Adaptive margins can help adjust for constancy violations that will inevitably occur in real clinical trials, while maintaining pre-specified levels of Type I error and power.