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Integrating SAS® and R to Perform Optimal Propensity Score Matching

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In studies where randomization is not possible, imbalance in baseline covariates (confounding by indication) is a fundamental concern. Propensity score matching (PSM) is a popular method to minimize this potential bias, matching individuals who received treatment to those who did not to reduce the imbalance in pre-treatment covariate distributions. PSM methods continue to advance as computing resources expand. Optimal matching, which selects the set of matches that minimizes the average difference in propensity scores between mates, has been shown to outperform less computationally intensive methods. However, many find the implementation daunting. SAS/IML® software allows the integration of optimal matching routines that execute in R, e.g. the R nbpMatching package. This paper walks through performing optimal PSM in SAS® through implementing R functions. It covers the propensity score creation in SAS, the matching procedure, and the post-matching assessment of covariate balance using SAS/STAT® 13.2 and SAS/IML procedures.

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