A Bayesian approach for sample size determination in method comparison studies.

Studies involving two methods for measuring a continuous response are regularly conducted in health sciences to evaluate agreement of a method with itself and agreement between methods. Notwithstanding their wide usage, the design of such studies, in particular, the sample size determination, has not been addressed in the literature when the goal is the simultaneous evaluation of intra- and inter-method agreement. We fill this need by developing a simulation-based Bayesian methodology for determining sample sizes in a hierarchical model framework. Unlike a frequentist approach, it takes into account uncertainty in parameter estimates. This methodology can be used with any scalar measure of agreement available in the literature. We demonstrate this for four currently used measures. The proposed method is applied to an ongoing proteomics project, where we use pilot data to determine the number of individuals and the number of replications needed to evaluate the agreement between two methods for measuring protein ratios. We also apply our method to determine the sample size for an experiment involving measurement of blood pressure.