Evaluating the Utility of Coarsened Exact Matching for Pharmacoepidemiology using Real and Simulated Claims Data.

Coarsened exact matching (CEM) is a matching method proposed as an alternative to other techniques commonly used to control confounding. We compared CEM with 3 techniques that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching and fine stratification by propensity score (FS). We evaluated confounding control and effect estimate precision using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999-2002) and Medicaid Analytic eXtract (2000-2007) databases (United States), and from simulated claims-based cohorts. CEM generally achieved the best covariate balance. However, it often led to high bias and low precision of the risk ratio due to extreme losses in study size and numbers of outcomes (i.e., sparse data bias) - especially with larger covariate sets. FS usually was optimal with respect to bias and precision and always created good covariate balance. Propensity score matching usually performed almost as well as FS, especially with higher index-exposure prevalence. The performance of Mahalanobis distance matching was relatively poor. These findings suggest that CEM, though it achieves good covariate balance, may not be optimal for large claims database studies with rich covariate information; it may be ideal if only a few (<10) strong confounders must be controlled. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2019.