Real-World Evidence, Causal Inference, and Machine Learning
Real-World Evidence, Causal Inference, and Machine Learning
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DOI：
10.1016/j.jval.2019.03.001
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发表时间：
2019-05-01
期刊：
影响因子：
4.5
通讯作者：
Crown, William H.
中科院分区：
文献类型：
--
作者：
Crown, William H.
目前对真实的世界证据（RWE）的关注发生在至少两个主要趋势正在融合的时候。首先是过去十年来在观察研究设计和方法方面取得的进展。第二，世界各地的许多大型观察性医疗保健数据库的发展正在创建改进的数据资产，以支持observational research.Objective：本文探讨了观察方法和研究设计的改进，以及越来越多的真实的世界数据的质量RWE的影响。这些事态发展是非常积极的。另一方面，非结构化数据（如医疗记录）以及通过合并多个数据资产创建的数据稀疏性不容易通过传统的健康服务研究统计方法处理。作为回应，机器学习方法作为分析大规模复杂dataset.Conclusions的潜在工具正在获得越来越多的关注：机器学习方法传统上用于分类和预测，而不是因果推理。机器学习的预测能力本身就很有价值。然而，使用机器学习进行因果推理仍在发展。机器学习可用于假设生成，然后应用传统的因果方法。但相对较新的发展，如有针对性的最大似然方法，直接将机器学习与因果推理相结合。
The current focus on real world evidence (RWE) is occurring at a time when at least two major trends are converging. First, is the progress made in observational research design and methods over the past decade. Second, the development of numerous large observational healthcare databases around the world is creating repositories of improved data assets to support observational research.Objective: This paper examines the implications of the improvements in observational methods and research design, as well as the growing availability of real world data for the quality of RWE. These developments have been very positive. On the other hand, unstructured data, such as medical notes, and the sparcity of data created by merging multiple data assets are not easily handled by traditional health services research statistical methods. In response, machine learning methods are gaining increased traction as potential tools for analyzing massive, complex datasets.Conclusions: Machine learning methods have traditionally been used for classification and prediction, rather than causal inference. The prediction capabilities of machine learning are valuable by themselves. However, using machine learning for causal inference is still evolving. Machine learning can be used for hypothesis generation, followed by the application of traditional causal methods. But relatively recent developments, such as targeted maximum likelihood methods, are directly integrating machine learning with causal inference.