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Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures
  1. Igor Burstyn1,
  2. Paul Gustafson2,
  3. Javier Pintos3,
  4. Jérôme Lavoué4,
  5. Jack Siemiatycki5
  1. 1 Department of Environmental and Occupational Health, Drexel University, Philadelphia, Pennsylvania, USA
  2. 2 Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
  3. 3 Department of Population Health, Centre de Recherche du CHUM, Montréal, Quebec, Canada
  4. 4 Department of Environmental and Occupational Health, University of Montréal, Montréal, Quebec, Canada
  5. 5 University of Montréal, Montréal, Quebec, Canada
  1. Correspondence to Dr Igor Burstyn, Department of Environmental and Occupational Health, Drexel University, Nesbitt Hall Room 6143215, Market Street, Philadelphia, PA 19104, USA; igor.burstyn{at}


Objectives Estimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR).

Methods The method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement.

Results Using interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7).

Conclusions The method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased.

  • bayesian statistics
  • retrospective exposure assessment
  • epidemiology
  • diesel fumes

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  • Contributors IB developed the concept for the paper, drafted initial manuscript and is responsible for the overall work as the guarantor. IB and PG developed and tested statistical method and code: this work was reviewed by JL. IB and JS elicited priors. JS and JP led collection, analysis and presentation of data in the illustrative example. All authors contributed to writing and approved the final text.

  • Funding Methodological work was unfunded; the case-control study was funded by a number of agencies,including the Health Canada, the National Cancer Institute of Canada, the Medical ResearchCouncil of Canada and the Canadian Institutes for Health Research, Fonds de la recherche ensante´ du Quebec, and JS was the recipient of a Canada Research Chair and holds the Guzzo-SRC Research Chair in Environment and Cancer.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.