American Journal of Epidemiology Advance Access originally published online on June 29, 2005
American Journal of Epidemiology 2005 162(3):279-289; doi:10.1093/aje/kwi192
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PRACTICE OF EPIDEMIOLOGY |
Adjusting Effect Estimates for Unmeasured Confounding with Validation Data using Propensity Score Calibration
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 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).
Often, data on important confounders are not available in cohort studies. Sensitivity analyses based on the relation of single, but not multiple, unmeasured confounders with an exposure of interest in a separate validation study have been proposed. In this paper, the authors controlled for measured confounding in the main cohort using propensity scores (PS's) and addressed unmeasured confounding by estimating two additional PS's in a validation study. The "error-prone" PS exclusively used information available in the main cohort. The "gold standard" PS additionally included data on covariates available only in the validation study. Based on these two PS's in the validation study, regression calibration was applied to adjust regression coefficients. This propensity score calibration (PSC) adjusts for unmeasured confounding in cohort studies with validation data under certain, usually untestable, assumptions. The authors used PSC to assess the relation between nonsteroidal antiinflammatory drugs (NSAIDs) and 1-year mortality in a large cohort of elderly persons. "Traditional" adjustment resulted in a hazard ratio for NSAID users of 0.80 (95% confidence interval (CI): 0.77, 0.83) as compared with an unadjusted hazard ratio of 0.68 (95% CI: 0.66, 0.71). Application of PSC resulted in a more plausible hazard ratio of 1.06 (95% CI: 1.00, 1.12). Until the validity and limitations of PSC have been assessed in different settings, the method should be seen as a sensitivity analysis.
bias (epidemiology); cohort studies; confounding factors (epidemiology); epidemiologic methods; propensity score calibration; research design
Abbreviations: CI, confidence interval; MCBS, Medicare Current Beneficiary Survey; NSAID, nonsteroidal antiinflammatory drug; PS, propensity score; PSC, propensity score calibration.
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