The continual reassessment method for multiple toxicity grades: a bayesian model selection approach.

Haitao Pan,Ling Wang,Chanjuan Li,Feng Zhang,Ying Yuan,Shemin Zhang,Jielai Xia,Cailin Zhu,Wenhong Zhang,Shyamal D Peddada

doi:10.1371/journal.pone.0098147

Abstract

Grade information has been considered in Yuan et al. (2007) wherein they proposed a Quasi-CRM method to incorporate the grade toxicity information in phase I trials. A potential problem with the Quasi-CRM model is that the choice of skeleton may dramatically vary the performance of the CRM model, which results in similar consequences for the Quasi-CRM model. In this paper, we propose a new model by utilizing bayesian model selection approach – Robust Quasi-CRM model – to tackle the above-mentioned pitfall with the Quasi-CRM model. The Robust Quasi-CRM model literally inherits the BMA-CRM model proposed by Yin and Yuan (2009) to consider a parallel of skeletons for Quasi-CRM. The superior performance of Robust Quasi-CRM model was demonstrated by extensive simulation studies. We conclude that the proposed method can be freely used in real practice.

Highlights

The primary goal of a phase I clinical trial of a new oncologic agent is to find a dose with acceptable toxicity, that is, to target the maximum tolerated dose (MTD)
In this paper we proposed the robust version of Quasi-CRM to model toxicity grades, and demonstrated by simulation that it is superior to the single skeleton version of Quasi-CRM
If one skeleton corresponds to the true toxicity probabilities, the Robust Quasi-CRM would perform very well, because it often performs to the best-performing Quasi-CRM

Summary

Introduction

The primary goal of a phase I clinical trial of a new oncologic agent is to find a dose with acceptable toxicity, that is, to target the maximum tolerated dose (MTD). Chappell and Bailey (2007) [5] (their proposed method is named as Quasi-CRM) used severity weights to convert toxicity grades to numerical scores. The recommended dose for the patient is the dose level with estimated score (the equivalent toxicity (ET) score) closest to the target score, obtained from a prespecified toxicity profile at the MTD This Quasi-CRM method has been demonstrated to be superior to the BT method in recommendation percentage of optimal dose for further studies. Garrett-Mayer and Bandyopadhyay (2012) [6] incorporated toxicity grades using a continuation ratio (CR) model in the likelihood-based CRM They demonstrated that the proposed method was better than that of dichotomous CRM counterpart.

The Method

Design

Findings

Conclusion