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American Journal of Epidemiology Advance Access published online on March 28, 2007
American Journal of Epidemiology, doi:10.1093/aje/kwm072
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American Journal of Epidemiology Copyright © 2007 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Invited Commentary: Advancing Propensity Score Methods in Epidemiology

J. Michael Oakes and Timothy R. Church

Correspondence to Dr. J. Michael Oakes, Division of Epidemiology and Community Health, Minnesota Population Center, University of Minnesota, Minneapolis, MN 55454 (e-mail: oakes{at}epi.umn.edu).

Received for publication December 13, 2006. Accepted for publication December 15, 2006.

Every epidemiologist knows that unmeasured confounding is a serious analytic problem, but practically speaking, there seems to be little one can do about it. In this issue of the Journal, Stürmer et al. (Am J Epidemiol 2007:000:000–00) offer a novel solution that combines propensity score matching methods with measurement error regression models. They call this technique "propensity score calibration" (PSC) and assess its strengths and limitations with simulated data. Their analyses demonstrate that PSC greatly improves inference when the critical assumption of surrogacy holds, but when surrogacy does not hold, PSC estimation can exacerbate bias relative to uncorrected propensity score models. The benefits of propensity score methods (and PSC) lie not only with potentially improved effect estimation but with conceptualization and practice as well.

bias (epidemiology); cohort studies; confounding factors (epidemiology); epidemiologic methods; models, statistical; propensity score calibration; research design

Abbreviations: IV, instrumental variable; OR, odds ratio; PSC, propensity score calibration


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T. Sturmer, S. Schneeweiss, K. J. Rothman, J. Avorn, and R. J. Glynn
Sturmer et al. Respond to "Propensity Score Methods in Epidemiology"
Am. J. Epidemiol., May 15, 2007; 165(10): 1122 - 1123.
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