Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions
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Type of Work12 pages
Citation of Original PublicationStuart EA, DuGof E, Abrams M, Salkever D, Steinwachs D. Estimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutions. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2013;1(3):4. DOI: http://doi.org/10.13063/2327-9214.1038
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Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)
Electronic health data sets, including electronic health records (EHR) and other administrative databases, are rich data sources that have the potential to help answer important questions about the effects of clinical interventions as well as policy changes. However, analyses using such data are almost always non-experimental, leading to concerns that those who receive a particular intervention are likely different from those who do not, in ways that may confound the effects of interest. This paper outlines the challenges in estimating causal effects using electronic health data, and offers some solutions, with particular attention paid to propensity score methods that help ensure comparisons between similar groups. The methods are illustrated with a case study describing the design of a study using Medicare and Medicaid administrative data to estimate the effect of the Medicare Part D prescription drug program among individuals with serious mental illness.
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