@article{ref1,
title="A new tool for case studies in Epidemiology - the Synthetic Control Method",
journal="Epidemiology",
year="2018",
author="Rehkopf, David H. and Basu, Sanjay",
volume="29",
number="4",
pages="503-505",
abstract="With the publication of &quot;Repeal of Comprehensive Background Check Policies and Firearm Homicide and Suicide,&quot; 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.<p /> <p>Language: en</p>",
language="en",
issn="1044-3983",
doi="10.1097/EDE.0000000000000837",
url="http://dx.doi.org/10.1097/EDE.0000000000000837"
}