Bayesian uncertainty-directed dose finding designs

We introduce Bayesian uncertainty-directed (BUD) designs for phase I–II dose finding trials. This class of designs assigns patients to candidate dose levels with the aim of maximizing explicit information metrics at completion of the trial, while avoiding the treatment of patients with toxic or ineffective dose levels during the trial. Explicit information metrics provide, at completion of the clinical study, accuracy measures of the final selection of optimal or nearly optimal dose levels. The BUD approach utilizes the decision theoretic framework and builds on utility functions that rank candidate dose levels. The utility of a dose combines the probabilities of toxicity events and the probability of a positive response to treatment. We discuss the application of BUD designs in two distinct settings; dose finding studies for single agents and precision medicine studies with biomarker measurements that allow dose optimization at the individual level. The approach proposed and the simulation scenarios used in the evaluation of BUD designs are motivated by a stereotactic body radiation therapy study in lung cancer at our institution.