Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching.

Abstract

Propensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM. Comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and β-blocker monotherapy identified from a large U.S. administrative claims database. One-to-one matching was conducted through PSM using nearest-neighbor matching (caliper=0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post-match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group. The eligible study population included 182 235 (ACEI: 129363; β-blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD <0.05 was associated with improved residual bias as compared to PSM. We recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates.