Borrowing historical information for non-inferiority trials on Covid-19 vaccines

Abstract

Non-inferiority vaccine trials compare new candidates to active controls that provide clinically significant protection against a disease. Bayesian statistics allows to exploit pre-experimental information available from previous studies to increase precision and reduce costs. Here, historical knowledge is incorporated into the analysis through a power prior that dynamically regulates the degree of information-borrowing. We examine non-inferiority tests based on credible intervals for the unknown effects-difference between two vaccines on the log odds ratio scale, with an application to new Covid-19 vaccines. We explore the frequentist properties of the method and we address the sample size determination problem.

Trial registration: ClinicalTrials.gov NCT04368728.

Keywords: Bayesian analysis; Hellinger distance; SARS-CoV-2; dynamic power prior; sample size determination; type-I error.