A Sequential Predictive Power Design for a COVID Vaccine Trial

Abstract

Medical investigations for therapeutics and vaccines for combating a pandemic such as COVID-19, call for flexible and adaptive trial designs that are capable of producing robust results amidst uncertainties. Here, we present a Bayesian sequential design to study the efficacy of Bacillus Calmette–Guérin (BCG) in providing protection against COVID-19 infections via its known “trained-immunity” mechanism. The main design consideration is to provide a framework to rapidly establish a proof-of-concept on the vaccine efficacy of BCG under a constantly evolving incidence rate and in the absence of prior efficacy data. The trial design is based on taking several interim looks and calculating the predictive power with the current cohort at each interim look. Decisions to stop the trial for futility or stopping enrollment for efficacy are made based on the current cohort predictive power computation. At any interim, if any of the above decisions cannot be taken then the study continues to enroll till the next interim look. Via extensive numerical studies, we show that the proposed design can achieve the desired frequentist operating characteristics, currently required by regulatory bodies while offering greater flexibility in terms of sample size and the ability to make robust interim decisions.

Keywords:

• Bayesian adaptive designs
• COVID-19
• Frequentist operating characteristics
• Predictive power
• Vaccine trials

Acknowledgments

We are very grateful to the anonymous reviewers for their helpful comments and insightful suggestions. We feel that this has resulted in a stronger article. We would also like to thank Prof. Yuan Ji, University of Chicago and Dr. Bernard Fritzell, Tuberculosis Vaccine Initiative, Lelystad, Netherlands, for helpful and encouraging comments.

Funding

The author(s) reported there is no funding associated with the work featured in this article.