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A simple method of determining confidence intervals for population attributable risk from complex surveys

Authors

  • Sundar Natarajan,

    Corresponding author
    1. VA New York Harbor Healthcare System, New York, NY 10010, U.S.A.
    2. Department of Medicine, New York University School of Medicine, New York, NY 10010, U.S.A.
    • 423 East 23rd Street, Room 11101-S, New York, NY 10010, U.S.A.
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  • Stuart R. Lipsitz,

    1. Department of Medicine, Harvard Medical School, Boston, MA 02115, U.S.A.
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  • Eric Rimm

    1. Departments of Epidemiology and Nutrition, Harvard School of Public Health, Boston, MA, U.S.A.
    2. Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, U.S.A.
    3. Harvard Medical School, Boston, MA 02115, U.S.A.
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Abstract

Methods to assess uncertainty in the estimated population attributable risk (PAR) by calculating 95 per cent confidence intervals (CIs) are not readily available in software for complex sample surveys. Using the Bonferroni inequality, a simple method to obtain CIs for the PAR is developed. The method is demonstrated using a simulation in a (2 × 2) table as well as a cohort study to calculate CIs for PAR of coronary heart disease death (using proportional hazards regression). This article demonstrates a straightforward, theoretically valid method of determining CIs for the PAR. Using this method, researchers analysing complex surveys can routinely provide a population perspective and a valid measure of the uncertainty for these estimates. Copyright © 2007 John Wiley & Sons, Ltd.

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