Anna Korpak
The intraclass correlation coefficient (ICC) is a measure of pairwise correlation between two observations within the same group.. Accounting for ICC is important for study design and for preserving inference in group-randomized trials. While analysis can account for ICC without actually estimating it, reasonable estimates for ICC are crucial to sample size calculations when planning cluster-based trials. The focus of this talk is the use of Bayesian methods for estimation of ICC in the case where the outcome variable of interest is binary. Using example data from a cross-sectional observational study in dentistry, with patients clustered within dental practices, we estimate ICC for various treatment-pattern and disease outcomes. Turner, Omar, & Thompson (2001) outline a general approach for Bayesian estimation of ICC for binary data and we adapt this to our scenario. We investigate the performance of this Bayes approach to ICC estimation compared to frequentist methods and examine the sensitivity of results to different choices and different parametrizations of the prior.
