A trial emulation approach for policy evaluations with group-level longitudinal data
In this talk, we will explore the use of target trial emulation and difference-in-difference approaches to estimate the causal effect of a policy, illustrated by stay-at-home mandates in the US during the pandemic.
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 emphasises 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 stylised 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.
Dr Eli Ben-Michael, Post-doctoral fellow, Department of Statistics, Harvard University
Eli Ben-Michael develops methods for causal inference, drawing from computational statistics, optimisation, and machine learning.
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