Indications for Propensity Scores and Review of their Use in Pharmacoepidemiology

Authors

  • Robert J. Glynn,

    Corresponding author
    1. Divisions of Pharmacoepidemiology and Pharmacoeconomics and of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, and Departments of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, MA, U.S.A.
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  • Sebastian Schneeweiss,

    1. Divisions of Pharmacoepidemiology and Pharmacoeconomics and of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, and Departments of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, MA, U.S.A.
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  • Til Stürmer

    1. Divisions of Pharmacoepidemiology and Pharmacoeconomics and of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, and Departments of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, MA, U.S.A.
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Author for correspondence: Robert J. Glynn, Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue, Boston, MA, U.S.A. (fax +1 617 734 1437, e-mail rglynn@rics.bwh.harvard.ed).

Abstract

Abstract: Use of propensity scores to identify and control for confounding in observational studies that relate medications to outcomes has increased substantially in recent years. However, it remains unclear whether, and if so when, use of propensity scores provides estimates of drug effects that are less biased than those obtained from conventional multivariate models. In the great majority of published studies that have used both approaches, estimated effects from propensity score and regression methods have been similar. Simulation studies further suggest comparable performance of the two approaches in many settings. We discuss five reasons that favour use of propensity scores: the value of focus on indications for drug use; optimal matching strategies from alternative designs; improved control of confounding with scarce outcomes; ability to identify interactions between propensity of treatment and drug effects on outcomes; and correction for unobserved confounders via propensity score calibration. We describe alternative approaches to estimate and implement propensity scores and the limitations of the C-statistic for evaluation. Use of propensity scores will not correct biases from unmeasured confounders, but can aid in understanding determinants of drug use and lead to improved estimates of drug effects in some settings.

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