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American Journal of Epidemiology Advance Access originally published online on March 28, 2007
American Journal of Epidemiology 2007 165(10):1110-1118; doi:10.1093/aje/kwm074
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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.

PRACTICE OF EPIDEMIOLOGY

Performance of Propensity Score Calibration—A Simulation Study

Til Stürmer1,2, Sebastian Schneeweiss1,3, Kenneth J. Rothman1,4,5, Jerry Avorn1 and Robert J. Glynn1,2,6

1 Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
2 Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
3 Department of Epidemiology, Harvard School of Public Health, Boston, MA
4 Department of Epidemiology, Boston University School of Public Health, Boston, MA
5 Research Triangle Institute, Research Triangle Park, NC
6 Department of Biostatistics, Harvard School of Public Health, Boston, MA

Correspondence to Dr. Til Stürmer, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA 02120 (e-mail: til.sturmer{at}post.harvard.edu).

Received for publication November 1, 2005. Accepted for publication July 13, 2006.

Confounding can be a major source of bias in nonexperimental research. The authors recently introduced propensity score calibration (PSC), which combines propensity scores and regression calibration to address confounding by variables unobserved in the main study by using variables observed in a validation study. Here, the authors assess the performance of PSC using simulations in settings with and without violation of the key assumption of PSC: that the error-prone propensity score estimated in the main study is a surrogate for the gold-standard propensity score (i.e., it contains no additional information on the outcome). The assumption can be assessed if data on the outcome are available in the validation study. If data are simulated allowing for surrogacy to be violated, results depend largely on the extent of violation. If surrogacy holds, PSC leads to bias reduction between 32% and 106% (>100% representing overcorrection). If surrogacy is violated, PSC can lead to an increase in bias. Surrogacy is violated when the direction of confounding of the exposure-disease association caused by the unobserved variable(s) differs from that of the confounding due to observed variables. When surrogacy holds, PSC is a useful approach to adjust for unmeasured confounding using validation data.

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

Abbreviations: OR, odds ratio; PSC, propensity score calibration; PSEP, error-prone propensity score; PSGS, gold-standard propensity score

Editor's note: An invited commentary on this article appears on page 1119, and the authors' response appears on page 1122.


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Related articles in Am. J. Epidemiol.:

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Am. J. Epidemiol. 2007 165: 1122-1123. [Extract] [FREE Full Text]  



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