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Bayesian sequential probability ratio test for vaccine efficacy trials

Author

Listed:
  • Erina Paul

    (Merck & Co., Inc.)

  • Santosh Sutradhar

    (Merck & Co., Inc.)

  • Jonathan Hartzel

    (Merck & Co., Inc.)

  • Devan V. Mehrotra

    (Merck & Co., Inc.)

Abstract

Designing vaccine efficacy (VE) trials often requires recruiting large numbers of participants when the diseases of interest have a low incidence. When developing novel vaccines, such as for COVID-19 disease, the plausible range of VE is quite large at the design stage. Thus, the number of events needed to demonstrate efficacy above a pre-defined regulatory threshold 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 interim analysis 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 sequential probability ratio test (SPRT) can be helpful to allow for multiple analyses while controlling for both type-I and type-II errors. In this article, we propose a Bayesian SPRT for designing a vaccine trial for comparing a test vaccine with a control assuming two Poisson incidence rates. We provide guidance on how to choose the prior distribution and how to optimize the number of events for interim analyses to maximize the efficiency of the design. Through simulations, we demonstrate how the proposed Bayesian SPRT performs better when compared with the corresponding frequentist SPRT. An R repository to implement the proposed method is placed at: https://github.com/Merck/bayesiansprt .

Suggested Citation

  • Erina Paul & Santosh Sutradhar & Jonathan Hartzel & Devan V. Mehrotra, 2024. "Bayesian sequential probability ratio test for vaccine efficacy trials," Computational Statistics, Springer, vol. 39(7), pages 3855-3880, December.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:7:d:10.1007_s00180-024-01458-5
    DOI: 10.1007/s00180-024-01458-5
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    References listed on IDEAS

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    1. Laurent, Stephane & Legrand, Catherine, 2012. "A bayesian framework for the ratio of two poisson rates in the context of vaccine efficacy trials Journal," LIDAM Reprints ISBA 2012023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Price, Robert M. & Bonett, Douglas G., 2000. "Estimating the ratio of two Poisson rates," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 345-356, September.
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