Bayesian Interval Based Dose Finding Designs and a Web-Based Statistical Tool

• Activity Number: 6 - New Methods and Software for Adaptive Designs
• Type: Invited
• Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yuan Ji* and Sue-Jane Wang and Shengjie Yang
• Companies: NorthShore University HealthSystem/University of Chicago and FDA and NorthShore University HealthSystem
• Keywords: Adaptive Designs ; Big Data ; mTPI-2 ; Occam's Razor ; Optimiality

Abstract

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 suboptimal decisions in small-sample inference such as dose finding. 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 (www.compgenome.org/NGDF) is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs through big data and crowd sourcing.