A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2

There has been an increasing interest in using interval-based Bayesian designs for dose finding, one of which is the modified toxicity probability interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.