A flexible Bayesian algorithm for sample size calculations in misclassified data

The problem of obtaining a flexible and easy to implement algorithm in order to derive the optimal sample size when the data are subject to misclassification is critical to practitioners. The topic is addressed from the Bayesian point of view where a special structure of the a priori parameter information is investigated. The proposed methodology is applied in specific examples.