Online Program

Return to main conference page
Thursday, February 15
PS1 Poster Session 1 and Opening Mixer Thu, Feb 15, 5:30 PM - 7:00 PM
Salons F-I

Combining Historical Data and Propensity Score Methods in Observational Studies to Improve Internal Validity (303636)

Heather Angier, Oregon Health & Science University 
David Ezekiel-Herrera, Oregon Health & Science University 
Nathalie Huguet, Oregon Health & Science University 
*Miguel Marino, Oregon Health & Science University 
Jean P. O'Malley, Oregon Health & Science University 
Lewis Raynor, OCHIN 
Teresa Schmidt, OCHIN 
Rachel Springer, Oregon Health & Science University 
Steele Valenzuela, Oregon Health & Science University 

Keywords: Observational Studies, Propensity Score Methods, Longitudinal Data Analysis, Electronic Health Records

Randomized experiments are the gold standard for establishing causality between a treatment and an outcome. For many research studies (e.g. studying the impact of gaining Medicaid public health insurance on health outcomes), ethics and/or feasibility prevent conduct of randomized experiments. In lieu of randomization, observational studies provide a rich opportunity to answer many questions. To produce reliable results, however, observational studies must appropriately control for confounders. We will describe how combining historical data and propensity score methods appropriately controls for confounders and thus increases the internal validity of observational studies. We demonstrate this easy-to-implement and widely applicable methodology in a study using electronic health records to evaluate the impact of gaining Medicaid through the Affordable Care Act (a.k.a. Obamacare) on diabetes-related biomarkers (i.e., hemoglobin A1c, low density lipoprotein). Specifically, we compare changes in biomarkers 24 months pre- to 24 months post-ACA among four insurance cohorts: 1) continuously insured, 2) continuously uninsured, 3) discontinuously insured, and 4) gained insurance post-ACA.