Bayer AG, Global RLE Strategies & Outcomes Data Generation, Aprather Weg 18a, 42096 Wuppertal, Germany.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, Utrecht, 3508 GA, the Netherlands.
J Clin Epidemiol. 2017 Nov;91:13-22. doi: 10.1016/j.jclinepi.2017.07.003. Epub 2017 Jul 14.
Pragmatic trials can improve our understanding of how treatments will perform in routine practice. In a series of eight papers, the GetReal Consortium has evaluated the challenges in designing and conducting pragmatic trials and their specific methodological, operational, regulatory, and ethical implications. The present final paper of the series discusses the operational and methodological challenges of data collection in pragmatic trials. A more pragmatic data collection needs to balance the delivery of highly accurate and complete data with minimizing the level of interference that data entry and verification induce with clinical practice. Furthermore, it should allow for the involvement of a representative sample of practices, physicians, and patients who prescribe/receive treatment in routine care. This paper discusses challenges that are related to the different methods of data collection and presents potential solutions where possible. No one-size-fits-all recommendation can be given for the collection of data in pragmatic trials, although in general the application of existing routinely used data-collection systems and processes seems to best suit the pragmatic approach. However, data access and privacy, the time points of data collection, the level of detail in the data, and the lack of a clear understanding of the data-collection process were identified as main challenges for the usage of routinely collected data in pragmatic trials. A first step should be to determine to what extent existing health care databases provide the necessary study data and can accommodate data collection and management. When more elaborate or detailed data collection or more structured follow-up is required, data collection in a pragmatic trial will have to be tailor-made, often using a hybrid approach using a dedicated electronic case report form (eCRF). In this case, the eCRF should be kept as simple as possible to reduce the burden for practitioners and minimize influence on routine clinical practice.
实用临床试验可以增进我们对治疗方法在常规实践中表现的理解。在一系列的八篇论文中,GetReal 联盟评估了设计和进行实用临床试验的挑战,以及它们在方法学、操作性、监管和伦理方面的具体影响。本系列的最后一篇论文讨论了实用临床试验中数据收集的操作性和方法学挑战。更实用的数据收集需要在提供高度准确和完整的数据与最小化数据录入和验证对临床实践的干扰程度之间取得平衡。此外,它应该允许有代表性的实践、医生和患者样本参与,这些患者在常规护理中接受治疗。本文讨论了与不同数据收集方法相关的挑战,并尽可能提出潜在的解决方案。对于实用临床试验中的数据收集,没有一刀切的建议,尽管一般来说,应用现有的常规数据收集系统和流程似乎最适合实用方法。然而,数据访问和隐私、数据收集的时间点、数据的详细程度以及对数据收集过程的理解不足,被确定为在实用临床试验中使用常规收集数据的主要挑战。第一步应该是确定现有的医疗保健数据库在多大程度上提供了必要的研究数据,并能够适应数据收集和管理。当需要更详细或更复杂的数据收集或更结构化的随访时,实用临床试验中的数据收集将不得不进行定制,通常使用专用的电子病例报告表(eCRF)的混合方法。在这种情况下,eCRF 应尽可能简单,以减轻医生的负担并最小化对常规临床实践的影响。
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