Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model

TL;DR

This paper evaluates innovative COVID-19 vaccine trial designs using epidemic simulation, demonstrating how targeted recruitment, early case data, and adaptive randomisation can improve trial efficiency and power.

Contribution

It introduces a novel simulation-based framework to assess trial design elements, including high-risk recruitment, early case data utilization, and response-adaptive randomisation strategies.

Findings

Ring recruitment improves trial power with high-risk contacts.

Early case data can be used more efficiently to enhance trial outcomes.

Certain adaptive randomisation methods preserve power while reducing infections.

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 randomised two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritising participants at high risk of infection, can result in substantial improvement in terms of power, if sufficiently many contacts of observed cases are at high risk. 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 randomisation, discussing their advantages and disadvantages in this two-arm context and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.