Credible sets for risk ratios in over-reported two-sample binomial data using the double-sampling scheme

We consider point and interval estimation for risk ratios based on two independent samples of binomial data subject to false positive misclassification. For such data it is well known that the model is unidentifiable. We consider incorporating training data obtained by using a double-sampling scheme to make the model identifiable. In this identifiable model, we propose a Bayesian method to make statistical inferences. In particular, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. The algorithm is illustrated using a real data example and further examined via Monte Carlo simulation studies.