The COVID-19 pandemic presents unprecedented challenges for drug developers seeking to evaluate the safety and efficacy of potential treatments for COVID-19. Clinical researchers must work quickly and adapt to emerging data. Building upon the FDA guidance document and Duke-Margolis’ critical path to rapid development and access to safe and effective COVID-19 therapeutics, this paper focuses on statistical opportunities for nimble and accelerated development for COVID-19 therapeutics. We focus on acceleration opportunities by way of increasing clinical trial efficiency, facilitating robust collection of key clinical outcomes, and enabling quantitative decision making to bring safe and effective therapeutics to the market. We present adaptive elements of trial designs which allow trials to evolve as new information emerges and discuss participation in master protocols to optimize use of resources, ensure scientific rigor and enhance interpretation of study results. Key clinical outcomes of importance to public health and core data elements are proposed to facilitate broader data sharing and robust decision making. Available statistical methods and efficient approaches to rapidly generate and synthesize meaningful evidence are presented contributing to quantitative decision making on the effectiveness and safety of COVID-19 therapeutics. In this paper, we share innovative trial designs, clinical outcome and data standards, and existing statistical methods to accelerate the development of COVID-19 therapeutics and recommend that they should be applied immediately in a harmonized fashion.

对于寻求评估COVID-19潜在疗法的安全性和有效性的药物开发人员来说，COVID-19大流行提出了前所未有的挑战。临床研究人员必须快速工作并适应新兴数据。基于FDA指导文件和Duke-Margolis的快速发展以及获得安全有效的COVID-19治疗剂的关键路径，本文着重于COVID-19治疗剂敏捷和加速发展的统计机会。我们通过提高临床试验效率，促进关键临床结果的可靠收集以及实现定量决策以将安全有效的疗法推向市场的方式，专注于加速机会。我们介绍了试验设计的适应性要素，这些要素可让试验随着新信息的出现而不断发展，并讨论对主规程的参与，以优化资源利用，确保科学严谨性并增强对研究结果的解释。提出了对公共卫生和核心数据要素至关重要的关键临床结果，以促进更广泛的数据共享和可靠的决策。提出了可利用的统计方法和有效方法来快速生成和合成有意义的证据，从而有助于对COVID-19治疗剂的有效性和安全性进行定量决策。在本文中，我们分享了创新的试验设计，临床结果和数据标准，