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Abstract:
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In the observational studies, misclassification of the exposure measurement is common and can substantially bias the association between an outcome and an exposure. Although misclassification in a single observational study has been well studied, few papers considered it in a meta-analysis. Meta-analyses of observational studies provide important evidence for health policy decisions, especially when large randomized controlled trials are unethical. It is imperative to properly account for misclassification in a meta-analysis to obtain valid point and interval estimates. In this paper, we propose a novel Bayesian approach to filling this methodological gap. We simultaneously synthesize two meta-analyses, with one on the association between a misclassified exposure and an outcome (main studies), and the other on the association between the misclassified exposure and the true exposure (validation studies). We extend the current scope of using external validation data by relaxing the ``transportability'' assumption through random effects models. The proposed model is illustrated using real data from a meta-analysis for the effect of cigarette smoking on diabetic peripheral neuropathy.
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