Confounder summary scores when comparing the effects of multiple drug exposures

Authors

  • Suzanne M. Cadarette PhD,

    Corresponding author
    1. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
    2. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
    • Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College Street, Toronto, Ontario, M5S 3M2, Canada.
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  • Joshua J. Gagne PharmD, MS,

    1. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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  • Daniel H. Solomon MD, MPH,

    1. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
    2. Division of Rheumatology, Immunology and Allergy; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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  • Jeffrey N. Katz MD, MS,

    1. Division of Rheumatology, Immunology and Allergy; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
    2. Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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  • Til Stürmer MD, MPH

    1. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
    2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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  • No conflict of interest is found in this paper.

Abstract

Purpose

Little information is available comparing methods to adjust for confounding when considering multiple drug exposures. We compared three analytic strategies to control for confounding based on measured variables: conventional multivariable, exposure propensity score (EPS), and disease risk score (DRS).

Methods

Each method was applied to a dataset (2000–2006) recently used to examine the comparative effectiveness of four drugs. The relative effectiveness of risedronate, nasal calcitonin, and raloxifene in preventing non-vertebral fracture, were each compared to alendronate. EPSs were derived both by using multinomial logistic regression (single model EPS) and by three separate logistic regression models (separate model EPS). DRSs were derived and event rates compared using Cox proportional hazard models. DRSs derived among the entire cohort (full cohort DRS) was compared to DRSs derived only among the referent alendronate (unexposed cohort DRS).

Results

Less than 8% deviation from the base estimate (conventional multivariable) was observed applying single model EPS, separate model EPS or full cohort DRS. Applying the unexposed cohort DRS when background risk for fracture differed between comparison drug exposure cohorts resulted in −7 to + 13% deviation from our base estimate.

Conclusions

With sufficient numbers of exposed and outcomes, either conventional multivariable, EPS or full cohort DRS may be used to adjust for confounding to compare the effects of multiple drug exposures. However, our data also suggest that unexposed cohort DRS may be problematic when background risks differ between referent and exposed groups. Further empirical and simulation studies will help to clarify the generalizability of our findings. Copyright © 2009 John Wiley & Sons, Ltd.

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