Center for Health Research, Kaiser Permanente Northwest, 3800 N, Interstate Ave, Portland , OR 97227, USA.
BMC Med Inform Decis Mak. 2012 Oct 19;12:117. doi: 10.1186/1472-6947-12-117.
The development of genomic tests is one of the most significant technological advances in medical testing in recent decades. As these tests become increasingly available, so does the need for a pragmatic framework to evaluate the evidence base and evidence gaps in order to facilitate informed decision-making. In this article we describe such a framework that can provide a common language and benchmarks for different stakeholders of genomic testing. Each stakeholder can use this framework to specify their respective thresholds for decision-making, depending on their perspective and particular needs. This framework is applicable across a broad range of test applications and can be helpful in the application and communication of a regulatory science for genomic testing. Our framework builds upon existing work and incorporates principles familiar to researchers involved in medical testing (both diagnostic and prognostic) generally, as well as those involved in genomic testing. This framework is organized around six phases in the development of genomic tests beginning with marker identification and ending with population impact, and highlights the important knowledge gaps that need to be filled in establishing the clinical relevance of a test. Our framework focuses on the clinical appropriateness of the four main dimensions of test research questions (population/setting, intervention/index test, comparators/reference test, and outcomes) rather than prescribing a hierarchy of study designs that should be used to address each phase.
基因组检测的发展是近几十年来医学检测领域最重要的技术进步之一。随着这些检测的日益普及，我们需要一个实用的框架来评估证据基础和证据差距，以便为知情决策提供便利。在本文中，我们描述了这样一个框架，它可以为基因组检测的不同利益相关者提供共同的语言和基准。每个利益相关者都可以根据自己的观点和特定需求，使用这个框架来确定自己的决策阈值。这个框架适用于广泛的测试应用，并有助于基因组测试的监管科学的应用和交流。我们的框架建立在现有工作的基础上，并纳入了一般涉及医疗检测（诊断和预后）的研究人员以及涉及基因组检测的研究人员所熟悉的原则。该框架围绕着基因组检测开发的六个阶段展开，从标志物识别开始，到人群影响结束，并强调了在确定测试的临床相关性时需要填补的重要知识空白。我们的框架侧重于测试研究问题的四个主要维度（人群/环境、干预/指标检测、对照/参考检测和结果）的临床适宜性，而不是规定应该用于解决每个阶段的研究设计的层次结构。
