Bunning Bryan J, Hedlin Haley, Chen Jonathan H, Ciolino Jody D, Ferstad Johannes Opsahl, Fox Emily, Garcia Ariadna, Go Alan, Johari Ramesh, Lee Justin, Maahs David M, Mahaffey Kenneth W, Opsahl-Ong Krista, Perez Marco, Rochford Kaylin, Scheinker David, Spratt Heidi, Turakhia Mintu P, Desai Manisha
Quantitative Sciences Unit, Stanford University, Stanford, CA, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
J Clin Transl Sci. 2023 Aug 2;7(1):e179. doi: 10.1017/cts.2023.582. eCollection 2023.
Clinical trials provide the "gold standard" evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources - data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.
Three examples of real-world trials that leverage different types of data sources - wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.
Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.
Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.
临床试验为推动医学实践提供了“金标准”证据，即便其在不断发展以整合真实世界数据来源。现代临床试验越来越多地纳入真实世界数据来源——并非用于研究且通常在自由生活环境中收集的数据。我们将纳入真实世界数据来源的试验称为真实世界试验。此类试验可能有潜力提高研究结果的普遍性，促进务实的研究设计，并评估真实世界中的有效性。然而，真实世界试验与传统试验在设计、实施和执行方面的关键差异在数据管理方面会产生影响，可能威胁到它们所期望的严谨性。
描述了三项利用不同类型数据来源（可穿戴设备、医疗设备和电子健康记录）的真实世界试验实例。得出了适用于所有三项试验与数据和安全监测委员会（DSMBs）关系的关键见解。
针对四个主题领域给出了见解和建议：A. DSMB的职责；B. DSMB的组成；C. 启动前活动；D. 启动后活动。我们建议更加强化并额外关注数据完整性。
临床试验可受益于纳入真实世界数据来源，这有可能提高研究结果的普遍性以及整体试验规模和效率。然而，这些数据呈现出一定程度的信息复杂性，严重依赖强大的数据科学基础设施。数据和安全监测的性质必须不断演变以适应新的试验场景，从而保护临床试验的严谨性。
