Literaturnachweis - Detailanzeige
| Autor/inn/en | Ben-Michael, Eli; Feller, Avi; Stuart, Elizabeth A. |
|---|---|
| Titel | A Trial Emulation Approach for Policy Evaluations with Group-Level Longitudinal Data |
| Quelle | (2021), (28 Seiten)
PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | COVID-19; Pandemics; Public Policy; Disease Control; Data Analysis; Epidemiology |
| Abstract | 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. [This paper was published in "Epidemiology" v32 n4 Jul 2021.] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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