Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available
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DOI：
10.1093/aje/kwv254
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发表时间：
2016-04-15
影响因子：
5
通讯作者：
Robins, James M.
中科院分区：
文献类型：
--
作者：
Hernan, Miguel A.;Robins, James M.
理想情况下，有关相对有效性或安全性的问题将通过适当设计和实施的随机实验来回答。当我们不能进行随机实验时，我们分析观察数据。从大型观察数据库（大数据）中得出的因果推断可以看作是一种模仿随机实验的尝试——目标实验或目标试验——这将回答兴趣问题。当目标是指导几种策略之间的决策时，观察数据的因果分析需要根据它们对特定目标试验的模拟程度进行评估。我们概述了一个使用大数据进行比较有效性研究的框架，使目标试验更加明确。该框架引导反事实理论来比较持续治疗策略的效果，组织分析方法，为观察性研究的批评提供结构化的过程，并有助于避免常见的方法缺陷。
Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.