Abstract
In oncology, dose escalation is often carried out to search for the maximum tolerated dose (MTD) in phase I clinical trials. We propose a Bayesian hybrid dose-finding method that inherits the robustness of model-free methods and the efficiency of model-based methods. In the Bayesian hypothesis testing framework, we compute the Bayes factor and adaptively assign a dose to each cohort of patients based on the adequacy of the dose-toxicity information that has been collected thus far. If the data observed at the current treatment dose are adequately informative about the toxicity probability of this dose (e.g. whether this dose is below or above the MTD), we make the decision of dose assignment (e.g. either to escalate or to de-escalate the dose) directly without assuming a parametric dose-toxicity curve. If the observed data at the current dose are not sufficient to deliver such a definitive decision, we resort to a parametric dose-toxicity curve, such as that of the continual reassessment method (CRM), in order to borrow strength across all the doses under study to guide dose assignment. We examine the properties of the hybrid design through extensive simulation studies, and also compare the new method with the CRM and the '3 + 3' design. The simulation results show that our design is more robust than parametric model-based methods and more efficient than nonparametric model-free methods.