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Strategies for Assessing and Addressing Confounding

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Related Experiment Videos

Bayesian adjustment for measurement error in continuous exposures in an individually matched case-control study.

Gabriela Espino-Hernandez1, Paul Gustafson, Igor Burstyn

  • 1Department of Statistics, University of British Columbia, Vancouver, BC, Canada.

BMC Medical Research Methodology
|May 17, 2011
PubMed
Summary

This study introduces a Bayesian method to correct for measurement error in epidemiological studies, specifically for multiple continuous exposures in matched case-control studies. The method is feasible and can be implemented in statistical software.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Environmental Health

Background:

  • Measurement error in explanatory variables is common in epidemiological studies.
  • Individually matched case-control studies are susceptible to bias from measurement error in continuous exposures.
  • Previous research on correcting for measurement error in multiple continuous exposures within this study design is limited.

Purpose of the Study:

  • To develop and illustrate a Bayesian method for correcting measurement error in multiple continuous exposures within individually matched case-control studies.
  • To apply the method to a study investigating the association between maternal thyroid hormone levels, perfluorinated acid exposure, and hypothyroxinemia risk.
  • To compare results from the proposed measurement error correction method with a naive analysis.

Main Methods:

  • A Bayesian approach incorporating a classical measurement error model for exposures and a conditional logistic regression disease model.
  • Inclusion of a random-effect exposure model to account for exposure variability.
  • Utilization of prior distributions and quality control data to estimate measurement error, with posterior distributions and credible intervals computed for odds ratios.

Main Results:

  • The developed Bayesian method is feasible and implementable using statistical software.
  • Correction for measurement error in the perfluorinated acids study showed minimal adjustment for the observed error levels.
  • Sensitivity analysis indicated that substantial adjustments would arise with larger assumed measurement errors.

Conclusions:

  • The proposed Bayesian method, utilizing a random-effect exposure model, justifies the use of conditional logistic regression in the presence of measurement error for multiple continuous exposures in matched case-control studies.
  • The method is successfully implementable in WinBUGS for correcting studies with several mismeasured continuous exposures under a classical measurement error model.