Online Program

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Wednesday, January 10
Wed, Jan 10, 5:30 PM - 7:00 PM
Crystal Ballroom CD & Prefunction
Welcome Reception & Poster Session I

WITHDRAWN: Novel Propensity Score Matching Method for Including Non-normal Covariates (304255)

Aylin Altan, OptumLabs 
Alex Kravetz, OptumLabs 
Vijay Nori, OptumLabs 
Charlotte Yeh, AARP Services Inc 

Keywords: propensity score matching, matching, methods, causal

Background: Propensity score matching (PSM) is often employed to address selection bias. While PSM leads to similar 1st and 2nd moments for matched variables, the underlying distribution can remain different between groups. We report a novel PSM variation, which addresses the non-normal distribution of health expenditures. Methods: Typically, case and control observations are only considered for matching if they are in within a specified caliper distance of a PS derived from logistic regression. To obtain the matched population, we utilize a weighted bipartite graph; potential matches are assigned an edge weight equal to the absolute value of the difference in cost. The maximum weight matching, obtained via the Hungarian algorithm, yields a cohort with optimally matched cost distributions. Results: Using a naïve greedy nearest neighbor algorithm, underlying cost distributions for the matched cohorts differed (median: $2,947 vs $2,342) vs after applying our method (median: $2,947 vs $2,836). Conclusion: Cohorts well-matched on prior medical cost is critical in assessment of economic outcomes. Given the distribution of costs, standard matching techniques may not be sufficient.