The Certainty Framework for Assessing Real-World Data in Studies of Medical Product Safety and Effectiveness
The Certainty Framework for Assessing Real-World Data in Studies of Medical Product Safety and Effectiveness
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DOI：
10.1002/cpt.2045
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发表时间：
2020-10-08
影响因子：
6.7
通讯作者：
Toh, Sengwee
中科院分区：
文献类型：
--
作者：
Cocoros, Noelle M.;Arlett, Peter;Toh, Sengwee
在临床和监管决策中使用真实世界数据的一个基本问题是：我们必须有多确定用于捕获暴露、结果、队列定义特征或混杂因素的算法是我们想要的？我们提供了一个实用的框架，以帮助研究人员和监管机构通过研究变量对一系列数据源评估和分类真实世界数据的适用性。必须在每个研究变量、所研究的具体问题、研究设计和手头决策的背景下考虑三个确定性水平（最佳、充分和可能）。
A fundamental question in using real-world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort-defining characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit-for-purposefulness of real-world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.