Bayesian estimation of log odds ratios over two-way contingency tables with intraclass correlated cells

Abstract

In this article, a Bayesian approach is proposed for the estimation of log odds ratios and intraclass correlations over a two-way contingency table, including intraclass correlated cells. Required likelihood functions of log odds ratios are obtained, and determination of prior structures is discussed. Hypothesis testing for log odds ratios and intraclass correlations by using the posterior simulations is outlined. Because the proposed approach includes no asymptotic theory, it is useful for the estimation and hypothesis testing of log odds ratios in the presence of certain intraclass correlation patterns. A family health status and limitations data set is analyzed by using the proposed approach in order to figure out the impact of intraclass correlations on the estimates and hypothesis tests of log odds ratios. Although intraclass correlations are small in the data set, we obtain that even small intraclass correlations can significantly affect the estimates and test results, and our approach is useful for the estimation and testing of log odds ratios in the presence of intraclass correlations.

Keywords:

• Bayes factor
• categorical data
• credible interval
• cross table
• neighborhood-level disease clustering
• epidemiology
• familial correlation
• normal distribution
• odds ratio
• population genetics
• prior distribution

Acknowledgements

We thank three referees for constructive criticisms and suggestions that significantly improved the clarity and quality of the manuscript.