Infectious DiseasesA Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal DataBen-Michael, Elia; Feller, Avib,c; Stuart, Elizabeth A.d Author Information From the aInstitute for Quantitative Social Science, Harvard University, Cambridge, MA bDepartment of Statistics, University of California, Berkeley, CA cGoldman School of Public Policy, University of California, Berkeley, CA dDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. Submitted June 12, 2020; accepted March 31, 2021 E.B.-M. was supported by a National Science Foundation Research and Training Grant #1745640. A.F.s time was supported by the Institute of Education Sciences, US Department of Education, through Grant R305D200010. E.S.’s time was supported by the National Institutes of Health through the RAND Center for Opioid Policy Tools and Information Center (P50DA046351) and a Johns Hopkins University Discovery Award. Description of the process by which someone else could obtain the data and computing code required to replicate the results reported in your submission: Replication data and code are available at https://github.com/ebenmichael/policy-trial-emulation. The authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Correspondence: Elizabeth Stuart, 624 N. Broadway, Room HH839, Baltimore, MD 21205. E-mail: [email protected]. Epidemiology: July 2021 - Volume 32 - Issue 4 - p 533-540 doi: 10.1097/EDE.0000000000001369 Buy SDC Metrics Abstract 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. Although 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 nonexperimental 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. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.