Citation

Rehkopf DH, Basu S. Epidemiology 2018; 29(4): 503-505.

Affiliation

Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA (DHR); Primary Care and Outcomes Research, Stanford University School of Medicine, Stanford, CA (SB).

Copyright

(Copyright © 2018, Lippincott Williams and Wilkins)

DOI

10.1097/EDE.0000000000000837

PMID

29613871

Abstract

With the publication of "Repeal of Comprehensive Background Check Policies and Firearm Homicide and Suicide," the synthetic control method joins a group of useful statistical methods for the analysis of observational data imported to the field of epidemiology from economics and political science. These approaches include fixed effects (2), difference in differences (2), regression discontinuity (3) and instrumental variables (4). This expansion of methodological tools has been critical for the development for epidemiology as the discipline has expanded to consider the analysis of exposures that are not easily subjected to true random assignment, and for which unmeasured confounders plausibly exist. Furthermore, for social epidemiology, these methods help to facilitate a consequential approach (5,6), as the subfield moves to evaluating the impacts of social and economic policies on health. The importance of considering the synthetic control method for epidemiologic questions is that when randomization is difficult or impossible, and there are no available instrumental variables, the tools available for analysis are currently limited. This is particularly true when only one or a small number of units are treated.

Language: en