Bayesian analysis of correlated misclassified binary data

A Bayesian analysis for a random effect binary logistic regression model in the presence of misclassified data is considered. The introduction of a random effect captures the possible correlation among the binary data in each covariate pattern and hence may provide a good alternative to standard models in terms of overall fit. Markov Chain Monte Carlo methods are applied to perform the computations needed to draw inferences and make model assessment, through an illustrative example involving a real medical data set.