A Bayesian approach to strengthen inference for case-control studies with multiple error-prone exposure assessments

Jing Zhang, Stephen R. Cole, David B. Richardson, Haitao Chu

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

In case-control studies, exposure assessments are almost always error-prone. In the absence of a gold standard, two or more assessment approaches are often used to classify people with respect to exposure. Each imperfect assessment tool may lead to misclassification of exposure assignment; the exposure misclassification may be differential with respect to case status or not; and, the errors in exposure classification under the different approaches may be independent (conditional upon the true exposure status) or not. Although methods have been proposed to study diagnostic accuracy in the absence of a gold standard, these methods are infrequently used in case-control studies to correct exposure misclassification that is simultaneously differential and dependent. In this paper, we proposed a Bayesian method to estimate the measurement-error corrected exposure-disease association, accounting for both differential and dependent misclassification. The performance of the proposed method is investigated using simulations, which show that the proposed approach works well, as well as an application to a case-control study assessing the association between asbestos exposure and mesothelioma.

Original languageEnglish (US)
Pages (from-to)4426-4437
Number of pages12
JournalStatistics in Medicine
Volume32
Issue number25
DOIs
StatePublished - Nov 10 2013

Keywords

  • Case-control study
  • Dependent
  • Differential
  • Gold standard
  • Misclassification

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