Designing Vaccine Efficacy Trials with Interim Analyses Using Bayesian SPRT

• Activity Number: 123 - The Early Bird Catches the Worm: Sequential Decision-Making as a Framework in Time of COVID-19 Pandemic
• Type: Invited
• Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Caucus for Women in Statistics
• Author(s): Erina Paul* and Santosh Sutradhar and Jonathan Hartzel and Devan V. Mehrotra
• Companies: Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc.
• Keywords: SPRT; Vaccine efficacy; Poisson rates; Interim analysis

Abstract

Designing a vaccine efficacy (VE) trials requires recruiting large number of participants when the disease of interest have low incidence. When developing novel vaccines, such as for COVID-19 disease, it is typically not known what the efficacy of the vaccine will be at the time the trial is designed. Thus, the number of events needed to demonstrate efficacy can be difficult to predict and the time needed to accrue the necessary events can often be long. Therefore, it is advantageous to evaluate the efficacy at earlier time-points in the trial to potentially allow the trials to stop early for overwhelming VE or futility. In such cases, incorporating interim analyses through the use of the SPRT can be helpful to allow for multiple analyses while controlling for both type-I error and type-II error. In this research, we propose a Bayesian SPRT for designing a vaccine trial for comparing a test vaccine with a control assuming two Poisson incidence rates. Through simulations, we demonstrate how the proposed Bayesian SPRT using suggested method of selecting a prior and performing interim analysis at optimum interim time-points performs better when compared with the frequentist SPRT.