Propensity score methodology for confounding control in health care utilization databases

Abstract

Propensity score (PS) methodology is a common approach to control for confounding in nonexperimental studies of treatment effects using health care utilization databases. This methodology offers researchers many advantages compared with conventional multivariate models: it directly focuses on the determinants of treatment choice, facilitating the understanding of the clinical decision-making process by the researcher; it allows for graphical comparisons of the distribution of propensity scores and truncation of subjects without overlapping PS indicating a lack of equipoise; it allows transparent assessment of the confounder balance achieved by the PS at baseline; and it offers a straightforward approach to reduce the dimensionality of sometimes large arrays of potential confounders in utilization databases, directly addressing the “curse of dimensionality” in the context of rare events. This article provides an overview of the use of propensity score methodology for pharmacoepidemiologic research with large health care utilization databases, covering recent discussions on covariate selection, the role of automated techniques for addressing unmeasurable confounding via proxies, strategies to maximize clinical equipoise at baseline, and the potential of machine-learning algorithms for optimized propensity score estimation. The appendix discusses the available software packages for PS methodology. Propensity scores are a frequently used and versatile tool for transparent and comprehensive adjustment of confounding in pharmacoepidemiology with large health care databases.