A Quantitative Bias Analysis to Assess Constancy Assumption in Noninferiority Trials Using Bayesian Hierarchical Models

Abstract

Noninferiority (NI) trial designs are often used to compare a new treatment with an active control for treating diseases for which use of a placebo control in a trial is ethically unacceptable. The validity of conclusion from an NI trial relies on some critical assumptions such as constancy assumption. This assumption, often unverifiable in a two-arm NI trial without a placebo control, refers to as the similarity of the active control effect in the current NI trial to that in the historical trials. Violation of the constancy assumption may lead to not only the inflation of Type I error, but also invalid conclusion of the NI trial. In this article, we propose a probabilistic bias analysis of historical data using the Bayesian hierarchical modeling framework to assess heterogeneity of active control effect and hence the constancy assumption. Both random sampling error and heterogeneity in the active control effect across historical trials will be accounted in the analysis of the current NI trial. The proposed method is illustrated using a real example of NI trial with a binary endpoint.