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

Abstract

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, although it achieves good covariate balance, might not be optimal for large claims-database studies with rich covariate information; it might be ideal if only a few (<10) strong confounders must be controlled.

Outcomes of COVID‐19 in hospitalized solid organ transplant recipients compared to a matched cohort of non‐transplant patients at a national healthcare system in the United States

“…Further, we sought to match a discrete list of static covariates available at baseline that reflect key risk factors associated with mortality in COVID‐19. CEM is expected to produce high performance relative to other matching options in this scenario 35 …”

“…CEM is expected to produce high performance relative to other matching options in this scenario. 35 This study also has its limitations. First, given this is a large cohort retrospective study, data were collected from EHR systems, which precluded the level of detail possible with a manual medical chart review.…”

Gender Differences in Intra-Household Financial Decision-Making: An Application of Coarsened Exact Matching

“…CEM also meets the congruence principle, with the data space and analysis space being the same, and reduces model dependence (Blackwell et al 2010). Studies with both real and simulated data, moreover, have shown that CEM is superior to other matching methods in regards to achieving the best balance (Fullerton et al 2016;Iacus et al 2012;King and Nielsen 2019;Ripollone et al 2020). Furthermore, CEM is considered most appropriate to use when the covariates include both continuous and discrete variables as well as so-called mixed variables, i.e., continuous variables with natural breakpoints (King and Nielsen 2019), such as is the case with the various couple differentials.…”

“…CEM, as outlined above, comprises four basic steps: first, the covariates are coarsened; second, exact matching is implemented with the coarsened data; third, unmatched units in bins that do not contain units of the opposite exposure status, i.e., treatment or control, are eliminated; finally, SATT is estimated using the matched dataset (Ripollone et al 2020). We now, in turn, discuss our approach to each step.…”

Cost Savings Associated With Palliative Care Among Older Adults With Advanced Cancer

“…This matching procedure can produce a more balanced match than other strategies, 31 and when compared to traditional propensity score matching reduces bias when less than 10 strong confounders need to be matched. 33 A detailed description of this procedure has been described elsewhere. 32 Patients were matched on identified confounders: age, sex, year of diagnosis, SEER registry region, Charlson score category (0, 1, 2, 3þ), receipt of care at a teaching hospital, and survival time from diagnosis to death.…”