A two-stage Bayesian adaptive design for minimum effective dose (MinED)-based dosing-finding trials

Abstract

Conventional phase I designs for finding a phase II recommended dose (P2RD) based on toxicity alone is problematic because the maximum tolerated dose (MTD) is not necessarily the optimal dose. Instead, recently attention has been given to find the minimum effective dose (MinED) - defined as the lowest effective dose. Traditional paradigms for the MinED studies are conducted as dose-ranging or dose-response trials which involve several doses and randomize patients among doses to find the MinED. An alternative approach for the MinED study is the so-called MinED-based dose-finding study, in which instead of conducting hypothesis testings and without power analysis, this kind of trial conduct dose escalation/de-escalation to target a pre-set MinED target. In this study, we propose a new Bayesian two-stage adaptive design schema based on framework of the interval-based phase I method. The proposed method is model-free without curve pre-specifications, which is suitable for various dose-efficacy relationships. The proposed method shows desirable theoretical finite property of semi-coherence and large sample property of consistency. A random scenario generative algorithm for the MinED has also been proposed for extensive simulation studies, which demonstrated desirable performances of the proposed method. An R package "MinEDfind" and a Shiny app have been developed for implementing the method.

Keywords: Bayesian adaptive dose-finding trial; Interval-based design; Minimum effective dose; Random scenario generative algorithm.