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Activity Number: 119 - SPEED: Government and Health Policy
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #330914 Presentation
Title: Optimal Matching Approaches in Health Policy Evaluations Under Rolling Enrollment
Author(s): Jonathan Gellar* and Jiaqi Li and Lauren Vollmer
Companies: Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research
Keywords: Propensity score; Health policy evaluation; Matching; Rolling enrollment; Causal inference

In many policy evaluations, propensity score matching is used to construct a matched control group that appears similar to the treatment group to minimize nonrandom selection bias. When subjects enroll in the treatment group on a rolling basis, several complications are introduced. These complications stem from the fact that, while each member of the treatment group is enrolled on a particular date, no analogous date exists for members of the control group. Thus, defining a single baseline period of matching covariates for the control group is more difficult. When treatment eligibility and enrollment is preceded by an acute event, such as a stroke or other type of hospitalization, the importance of properly aligning control subjects is compounded. We discuss several strategies to handle these complications, including a novel approach to optimize matching while disallowing a unique potential control to be matched to more than one treatment subject. This approach is available as the R package groupmatch, which is compatible with other popular matching packages such as optmatch and MatchIt.

Authors who are presenting talks have a * after their name.

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