Abstract
ObjectivesLittle is known about influences of sample selection on estimation in propensity score matching. The purpose of the study was to assess potential selection bias using one-to-one greedy matching versus optimal full matching as part of an evaluation of supportive housing in New York City (NYC).Study Design and SettingsData came from administrative data for 2 groups of applicants who were eligible for an NYC supportive housing program in 2007–09, including chronically homeless adults with a substance use disorder and young adults aging out of foster care. We evaluated the 2 matching methods in their ability to balance covariates and represent the original population, and in how those methods affected outcomes related to Medicaid expenditures.ResultsIn the population with a substance use disorder, only optimal full matching performed well in balancing covariates, whereas both methods created representative populations. In the young adult population, both methods balanced covariates effectively, but only optimal full matching created representative populations. In the young adult population, the impact of the program on Medicaid expenditures was attenuated when one-to-one greedy matching was used, compared with optimal full matching.ConclusionGiven covariate balancing with both methods, attenuated program impacts in the young adult population indicated that one-to-one greedy matching introduced selection bias.
Highlights
Propensity score matching has been widely used to reduce bias due to confounding in observational studies [1,2,3]
Given covariate balancing with both methods, attenuated program impacts in the young adult population indicated that one-to-one greedy matching introduced selection bias
This is because optimal full matching minimizes the total distance between treatment and control groups, whereas one-to-one greedy matching performs localized matching in which a person in the treatment/exposed group is sequentially matched with a person in the control group [5,6]
Summary
Propensity score matching has been widely used to reduce bias due to confounding in observational studies [1,2,3]. When addressing covariate imbalance via propensity score matching, optimal full matching has been shown to be more efficient than one-to-one greedy matching [5]. When evaluating public health interventions targeting certain populations, it is important to ensure comparability between the propensity score-matched population and the original population of interest. If they are systematically different due to the exclusion of unmatched subjects, external validity (or generalizability) may be reduced [8,9]
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