UBC Theses and Dissertations
Case-control studies with misclassified exposure : a Bayesian approach Saskin, Refik
When dealing with the case-control data, it is often the case that the exposure to a risk factor of interest is subject to miclassification. Methods for correcting the odds-ratio are available when the misclassification probabilities are known. In practice, however, good guesses rather than the exact values are available for these probabilities. We show that when these guesses are treated as exact even the smallest differencies between the true and guessed values can lead to very erroneous odds-ratio estimates. This problem is alleviated by a Bayesian analysis which incorporates the uncertainty about the misclassification probabilities as prior information. In practice, data on the exposure variable are quite often available from more than one source. We review three methods for improving the odds-ratio estimates that combine information from two sources. We then develop a Bayesian approach which is based on latent class analysis, and apply it to the sudden infant death syndrome data. The inference required the use of the Metropolis-Hastings algorithm and/or the Gibbs sampler.
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