Global Biostatistical Sciences, Sanofi Pasteur, Swiftwater, PA, USA.
Center for Device and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
Contemp Clin Trials. 2020 Aug;95:106073. doi: 10.1016/j.cct.2020.106073. Epub 2020 Jul 3.
In recent years, there has been a proliferation of regulatory and industry-wide initiatives on structured benefit-risk (BR) assessment. Examples of structured BR frameworks include the PrOACT-URL (Problem formulation, Objectives, Alternatives, Consequences, Trade-Offs, Uncertainties, Risk Attitude and Linked Decisions) from European Medicines Agency Work Package 3, multiple U.S. Food and Drug Administration guidance documents on benefit-risk assessment for medical devices, and U.S. Food and Drug Administration implementation plans for benefit-risk assessment in drug regulatory decision-making. In June 2016, the ICH Expert Working Group finalized the Common Technical Document (CTD) Section 2.5.6 on Benefit-Risk Evaluations. As a result of these efforts, the uptake and utilization of structured benefit-risk (BR) assessments has been increasing. However, the aforementioned BR frameworks are mostly qualitative in nature, and the utility of quantitative BR approaches has not been systemically explored, creating uncertainty about settings in which quantitative BR assessment (qBRA) could be optimally applied. In this paper, we will provide an overview of the current qBRA methods, discuss challenges of qBRA, and describe a structural qBRA framework. The performance of the described qBRA framework will be evaluated by simulations based on a case study.
近年来，关于结构化获益-风险（BR）评估的监管和全行业举措大量涌现。结构化 BR 框架的示例包括欧洲药品管理局工作包 3 的 PrOACT-URL（问题表述、目标、替代方案、后果、权衡、不确定性、风险态度和关联决策）、美国食品和药物管理局关于医疗器械获益-风险评估的多份指导文件，以及美国食品和药物管理局在药物监管决策中进行获益-风险评估的实施计划。2016 年 6 月，ICH 专家工作组最终确定了关于获益-风险评估的通用技术文件（CTD）第 2.5.6 节。由于这些努力，结构化获益-风险（BR）评估的采用和利用一直在增加。然而，上述 BR 框架主要是定性的，定量 BR 方法的实用性尚未得到系统探索，这对定量 BR 评估（qBRA）可以最佳应用的环境造成了不确定性。在本文中，我们将概述当前的 qBRA 方法，讨论 qBRA 的挑战，并描述一个结构 qBRA 框架。将通过基于案例研究的模拟来评估所描述的 qBRA 框架的性能。
