  To limit the spread of the novel coronavirus, governments across the world
implemented extraordinary physical distancing policies, such as stay-at-home
orders, and numerous studies aim to estimate their effects. 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, which we refer to as "policy trial
emulation." This 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.
