Fortin Stephen P, Schuemie Martijn
Observational Health Data Analytics, Janssen R&D, LLC, Raritan, New Jersey, USA.
Pharmacoepidemiol Drug Saf. 2022 Dec;31(12):1242-1252. doi: 10.1002/pds.5510. Epub 2022 Jul 20.
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.
倾向得分匹配（PSM）存在与自由度有限和协变量重叠相关的局限性。基数匹配（CM）作为一种优化算法，通过直接匹配协变量的边际分布克服了这些局限性。本研究比较了PSM和CM的性能。
从一个大型美国行政索赔数据库中识别出血管紧张素转换酶抑制剂（ACEI）和β受体阻滞剂单一疗法新使用者的比较队列研究。通过PSM使用最近邻匹配（卡尺 = 0.15）进行一对一匹配，并通过CM在比较组之间允许最大标准化均数差（SMD）为0、0.01、0.05和0.10。匹配协变量包括37项患者人口统计学和临床特征。观察到的协变量包括患者人口统计学以及所有观察到的既往疾病、药物暴露和诊疗程序。基于阴性对照结局实验的预期绝对系统误差评估残余混杂。在10%、1%、0.25%和0.125%的样本组中，比较了PSM和CM在匹配后患者留存率、匹配和观察到的协变量平衡以及残余混杂方面的情况。
符合条件的研究人群包括182235名患者（ACEI：129363名；β受体阻滞剂：56872名）。在所有分析中，CM实现了更好的患者留存率和匹配协变量平衡。PSM后，在10%和0.125%的样本组中，分别有1.6%和28.2%的匹配协变量不平衡。在匹配技术之间，未观察到观察到的协变量平衡有显著差异。与PSM相比，允许最大SMD<0.05的CM与残余偏倚改善相关。
当在一组临床相关协变量上进行匹配时，我们推荐使用平衡标准更严格的CM作为PSM的替代方法。
