A bivariate Bayesian framework for simultaneous evaluation of two candidate companion diagnostic assays in a new drug clinical trial

Abstract

Companion diagnostic tests play an important role in precision medicine. With the advancement of new technologies, multiple companion diagnostic tests can be rapidly developed in multiple platforms and use different samples to select patients for new treatments. Analytically validated assays must be clinically evaluated before they can be implemented in patient management. The status quo design for validating candidate assays is to employ one candidate assay to select patients for new drug clinical trial and then further evaluate the 2nd candidate assay in a bridging study. We propose a new enrollment strategy that employs two assays to select patients. We then develop a bivariate Bayesian approach that enables the totality of data to be used in evaluating whether these assays can be used independently or in a composite procedure in selecting right patients for new treatment. We demonstrate through simulations that when proper priors are available, the Bayesian approach is superior to classical methods in terms of statistical power.