Department of Biostatistics, University of Washington, Seattle, WA, USA.
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases (NIAID/NIH), Bethesda, MD, USA.
Clin Trials. 2021 Aug;18(4):391-397. doi: 10.1177/17407745211018613. Epub 2021 May 27.
Although several COVID-19 vaccines have been found to be effective in rigorous evaluation and have emerging availability in parts of the world, their supply will be inadequate to meet international needs for a considerable period of time. There also will be continued interest in vaccines that are more effective or have improved scalability to facilitate mass vaccination campaigns. Ongoing clinical testing of new vaccines also will be needed as variant strains continue to emerge that may elude some aspects of immunity induced by current vaccines. Randomized clinical trials meaningfully enhance the efficiency and reliability of such clinical testing. In clinical settings with limited or no access to known effective vaccines, placebo-controlled randomized trials of new vaccines remain a preferred approach to maximize the reliability, efficiency and interpretability of results. When emerging availability of licensed vaccines makes it no longer possible to use a placebo control, randomized active comparator non-inferiority trials may enable reliable insights.
In this article, "hybrid" methods are proposed to address settings where, during the conduct of a placebo-controlled trial, a judgment is made to replace the placebo arm by a licensed COVID-19 vaccine due to emerging availability of effective vaccines in regions participating in that trial. These hybrid methods are based on proposed statistics that aggregate evidence to formally test as well as to estimate the efficacy of the experimental vaccine, by combining placebo-controlled data during the first period of trial conduct with active-controlled data during the second period.
Application of the proposed methods is illustrated in two important scenarios where the active control vaccine would become available in regions engaging in the experimental vaccine's placebo-controlled trial: in the first, the active comparator's vaccine efficacy would have been established to be 50%-70% for the 4- to 6-month duration of follow-up of its placebo-controlled trial; in the second, the active comparator's vaccine efficacy would have been established to be 90%-95% during that duration. These two scenarios approximate what has been seen with adenovirus vaccines or mRNA vaccines, respectively, assuming the early estimates of vaccine efficacy for those vaccines would hold over longer-term follow-up.
The proposed hybrid methods could readily play an important role in the near future in the design, conduct and analysis of randomized clinical trials performed to address the need for multiple additional vaccines reliably established to be safe and have worthwhile efficacy in reducing the risk of symptomatic disease from SARS-CoV-2 infections.
尽管已经有几种 COVID-19 疫苗在严格的评估中被证明是有效的，并且在世界上的某些地区已经开始供应，但在相当长的一段时间内，它们的供应将不足以满足国际需求。人们还将继续关注那些更有效或具有更高可扩展性的疫苗，以方便大规模疫苗接种运动。随着不断出现的可能逃避当前疫苗诱导的某些免疫方面的变异株，也需要对新疫苗进行持续的临床测试。
在本文中，提出了“混合”方法来解决以下情况：在安慰剂对照试验进行期间，由于参与该试验的地区出现了有效疫苗，因此做出判断用已获得许可的 COVID-19 疫苗替代安慰剂组。这些混合方法基于提出的统计学方法，通过在试验进行的第一阶段结合安慰剂对照数据和第二阶段的活性对照数据，对实验疫苗的疗效进行正式检验和估计。
拟议的混合方法可以在不久的将来在设计、进行和分析随机临床试验中发挥重要作用，以满足对多种额外疫苗的需求，这些疫苗需要可靠地证明安全有效，能够降低 SARS-CoV-2 感染引起的症状性疾病的风险。
