A Bayesian approach to the analysis of asymmetric association for two-way contingency tables

Recently, a subcopula-based asymmetric association measure was developed for the variables in two-way contingency tables. Here, we develop a fully Bayesian method to implement this measure, and examine its performance using simulation data and several real data sets of colorectal cancer. We use coverage probabilities and lengths of the interval estimators to compare the Bayesian approach and a large-sample method of analysis. In simulation studies, we find that the Bayesian method outperforms the large-sample method on average, and provides either similar or improved results for the real data analyses.