An adaptive model switching approach for phase I dose-finding trials

作者：Daimon Takashi^*; Zohar Sarah

来源：Pharmaceutical Statistics, 2013, 12(4): 225-232.

DOI：10.1002/pst.1578

• model selection
• model comparison
• adaptive design
• maximum tolerated dose
• cancer clinical trial
• Bayesian inference
• phase I

Model-based phase I dose-finding designs rely on a single model throughout the study for estimating the maximum tolerated dose (MTD). Thus, one major concern is about the choice of the most suitable model to be used. This is important because the dose allocation process and the MTD estimation depend on whether or not the model is reliable, or whether or not it gives a better fit to toxicity data. The aim of our work was to propose a method that would remove the need for a model choice prior to the trial onset and then allow it sequentially at each patient%26apos;s inclusion. In this paper, we described model checking approach based on the posterior predictive check and model comparison approach based on the deviance information criterion, in order to identify a more reliable or better model during the course of a trial and to support clinical decision making. Further, we presented two model switching designs for a phase I cancer trial that were based on the aforementioned approaches, and performed a comparison between designs with or without model switching, through a simulation study. The results showed that the proposed designs had the advantage of decreasing certain risks, such as those of poor dose allocation and failure to find the MTD, which could occur if the model is misspecified.

• 出版日期2013-7