Quantifying Efficiency Gains of Innovative Designs of Two-Arm Vaccine Trials for COVID-19 Using an Epidemic Simulation Model

Rob Johnson*, Chris Jackson, Anne Presanis, Sofia S. Villar, Daniela De Angelis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements may be evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomized two-arm trials with a binary outcome. We show that “ring” recruitment strategies, prioritizing participants at an imminent risk of infection, can result in substantial improvement in terms of power in the model we present. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomization (RAR), discussing their advantages and disadvantages in the context of our model and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.

Original languageEnglish
JournalStatistics in Biopharmaceutical Research
DOIs
Publication statusAccepted/In press - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.

Keywords

  • Adaptive design
  • Network model
  • Response-adaptive randomization

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