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dc.contributor.authorStuart, Elizabeth A.
dc.contributor.authorDuGof, Eva
dc.contributor.authorAbrams, Michael
dc.contributor.authorSalkever, David
dc.contributor.authorSteinwachs, Donald
dc.date.accessioned2019-07-09T12:53:29Z
dc.date.available2019-06-12T18:25:07Z
dc.date.issued2013-12-01
dc.description.abstractElectronic 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.en_US
dc.description.sponsorshipThis research was supported by the National Institute of Mental Health (K25MH083846, Principal Investigator Stuart; R01MH079974, Principal Investigator Steinwachs). We thank Jack Clark for his expert SAS programming work on this project, and Pradeep Guin for his research assistant support.en_US
dc.description.urihttps://egems.academyhealth.org/articles/abstract/10.13063/2327-9214.1038/en_US
dc.format.extent12 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2x01g-vbqb
dc.identifier.citationStuart 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/10.13063/2327-9214.1038en_US
dc.identifier.urihttp://doi.org/10.13063/2327-9214.1038
dc.identifier.urihttp://hdl.handle.net/11603/14059
dc.language.isoen_USen_US
dc.publisherUbiquity Pressen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofOther Hilltop Institute (UMBC) Works
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC School of Public Policy
dc.relation.ispartofUMBC Sociology, Anthropology, and Health Administration Policy Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/*
dc.subjectPropensity scoresen_US
dc.subjectnon-experimental studyen_US
dc.subjectbig dataen_US
dc.titleEstimating Causal Effects in Observational Studies Using Electronic Health Data: Challenges and (some) Solutionsen_US
dc.typeTexten_US


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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Except where otherwise noted, this item's license is described as This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.