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.
翻译:在流行病期间对疫苗的临床试验面临特殊的挑战,例如要求迅速确定有效疫苗以控制该流行病的压力,以及时间空间变化的传染率对试验能力的影响。我们说明如何利用网络流行病和试验模拟模型来评价不同试验设计要素的操作特点,这种模型以COVID-19为基础,个别随机的双臂试验以二元结果为基础。我们表明,“环形”征聘战略,将感染风险高的参与者列为优先对象,如果所观察到的病例的接触次数足够多,就会大大改善权力。此外,我们引入一种新的方法,以便更有效地利用在试验中观察到的最早的感染病例的数据,这些病例的感染可能太早,无法预防疫苗。最后,我们比较了几种反应适应性随机分析方法,讨论其在这个两臂情况下的利弊,并找出特别的适应战略,以保持权力和估计特性,同时在有效疫苗的情况下,略有减少感染人数。