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合作平台试验抗击 COVID-19:为实现更好的社会效益的方法学和监管考虑。

Collaborative Platform Trials to Fight COVID-19: Methodological and Regulatory Considerations for a Better Societal Outcome.

机构信息

GlaxoSmithKline, Stevenage, Hertfordshire, UK.

Statistical Innovation, Data Science, and Artificial Intelligence, AstraZeneca R&D, Gothenburg, Sweden.

出版信息

Clin Pharmacol Ther. 2021 Aug;110(2):311-320. doi: 10.1002/cpt.2183. Epub 2021 Mar 16.

Abstract

For the development of coronavirus disease 2019 (COVID-19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID-19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life-saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time-varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence-generation initiatives when a positive return on investment is not met.

摘要

在当前的大流行期间,为了开发针对 2019 年冠状病毒病(COVID-19)的药物,速度至关重要,而证据质量则至关重要。尽管迅速启动了数千项 COVID-19 试验,但其中许多试验不太可能提供可靠的统计证据并符合监管标准(例如,由于缺乏随机化或功效不足)。这导致时间和资源的低效利用。通过更多的协调,这些试验中的大量患者可能已经为几种研究性治疗方法产生了令人信服的数据。比较几种药物与共享对照臂的协作平台试验是一种有吸引力的解决方案。这些试验可以利用各种适应性设计特征来加速救生治疗方法的发现。在本文中,我们讨论了几种可能的设计方案,通过模拟对其进行了说明,并讨论了一些挑战,例如目标人群的异质性、随时间变化的标准治疗以及在 II 期和 III 期试验中可能会有大量虚假假设被否定。我们提供了有关批准和报销的相应监管观点,并指出平台试验的最佳设计将根据我们的社会目标和利益相关者而有所不同。仓促批准可能会延迟更好替代方案的开发,而一味地寻找最有效的单一治疗方法可能会随着时间的流逝而间接减少挽救的生命数量。我们指出,当投资回报不理想时,有必要激励开发人员参与协作式证据生成计划。

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