Bayesian approach to partially validated binary regression with response and exposure misclassification.

Misclassification of epidemiological and observational data is a problem that commonly arises and can have adverse ramifications on the validity of results if not properly handled. Considerable research has been conducted when only the response or only the exposure are misclassified, while less work has been done on the simultaneous case. We extend previous frequentist work by investigating a Bayesian approach to dependent, differential misclassification models. Using a logit model with misclassified binary response and exposure variables and assuming a validation sub-sample is available, we compare the resulting confidence and credible intervals under the two paradigms. We compare the results under varying percentages of validation subsamples, 100% (ideal scenario), 25%, 15%, 10%, 5%, 2.5%, and 0% (naive scenario) of the overall sample size. We extend this work further be examining scenarios for which the assumptions may falter; we assume independent, differential misclassification, increase the overall sample size, and vary the influence of our priors from diffuse to concentrated.