A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data.
A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data.
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DOI：
10.1097/ede.0000000000001369
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发表时间：
2021-07-01
期刊：
影响因子：
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通讯作者：
Stuart EA
中科院分区：
文献类型：
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作者：
Ben-Michael E;Feller A;Stuart EA
为了限制新型冠状病毒的传播，世界各国政府实施了非同寻常的物理距离政策，例如居家令。许多研究旨在评估这些政策的影响。许多统计和计量经济学方法，如差异中的差异，利用重复测量和时间变化来估计政策效果，包括在COVID-19背景下。虽然这些方法在流行病学中不太常见，但流行病学研究人员已经习惯于在个人水平干预研究中处理类似的复杂性。靶向试验模拟强调需要在纳入和排除标准、协变量、暴露定义和结果测量以及这些变量的时间方面仔细设计非实验性研究。我们认为，政策评估组水平的纵向（“面板”）数据需要采取类似的仔细的研究设计，我们称之为政策试验仿真。这种方法在干预时间因管辖区而异时尤为重要;主要思想是为每个治疗队列（同时实施政策的州）单独构建目标试验，然后进行汇总。我们对国家级居家令对冠状病毒病例总数的影响进行了程式化分析。我们认为，面板方法的估计-正确的数据和仔细的建模和诊断-可以帮助增加我们对许多政策的理解，尽管这样做往往具有挑战性。
To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders. Numerous studies aim to estimate the effects of these policies. Many statistical and econometric methods, such as difference-in-differences, leverage repeated measurements and variation in timing to estimate policy effects, including in the COVID-19 context. While these methods are less common in epidemiology, epidemiologic researchers are well accustomed to handling similar complexities in studies of individual-level interventions. Target-trial emulation emphasizes the need to carefully design a non-experimental study in terms of inclusion and exclusion criteria, covariates, exposure definition, and outcome measurement — and the timing of those variables. We argue that policy evaluations using group-level longitudinal (“panel”) data need to take a similar careful approach to study design that we refer to as policy-trial emulation. This approach is especially important when intervention timing varies across jurisdictions; the main idea is to construct target trials separately for each treatment cohort (states that implement the policy at the same time) and then aggregate. We present a stylized analysis of the impact of state-level stay-at-home orders on total coronavirus cases. We argue that estimates from panel methods — with the right data and careful modeling and diagnostics — can help add to our understanding of many policies, though doing so is often challenging.