Robust Bayesian Sensitivity Analysis for Case–Control Studies with Uncertain Exposure Misclassification Probabilities

Mak Timothy Shin Heng (), Best Nicky () and Rushton Lesley ()
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Mak Timothy Shin Heng: Centre for Genomic Sciences, University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong
Best Nicky: Department of Epidemiology and Biostatistics, Imperial College London, London, UK
Rushton Lesley: Department of Epidemiology and Biostatistics, Imperial College London, London, UK

The International Journal of Biostatistics, 2015, vol. 11, issue 1, 135-149

Abstract: Exposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.

Keywords: misclassification; robust Bayes; case–control study; Bayesian (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/ijb-2013-0044

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