All Times EDT
Keywords: Predictive Bayes factor, Model-assisted designs, BOIN design, Keyboard design
Model-assisted designs are cutting edge adaptive designs to find the maximum tolerated dose (MTD) in phase I clinical trials. They enjoy superior performance comparable to more complicated, model-based adaptive designs, but with their decision rule pretabulated, they can be implemented as simply as the conventional algorithmic designs. In this work, we propose the posterior predictive (PoP) design, a novel model-assisted design to exploit Bayesian interval hypothesis testing, and develop a freely accessible R package (PoPdesign) to better facilitate the trial design application. Our work moves beyond the previous model-assisted designs by theoretically achieving consistency in selecting the true MTD and global optimality in dose transition. The simulation studies demonstrate that the PoP design can achieve significant improvement in operating characteristics to identify the MTD. Therefore, the PoP design provides a useful and convenient upgrade to the prevalent model-assisted designs.