A modified Huber loss function for continual reassessment methods in clinical trials

Abstract

In dose-finding (DF) trials, methods for discovering an optimal criterion that controls toxicity while demonstrating a potential for efficacy have been the subject of statistical research. Although continual reassessment methods (CRMs) have been utilized, the consensus among practitioners and clinical trial specialists is that there is always room for improvement with CRMs. Within the paradigm of a full-Bayesian method for CRMs, we examine the performance of dose selection algorithms based on a family of loss functions defined in Huber (The Annals of Mathematical Statistics, vol. 35, issue 1, pp. 73–101, 1964), namely, the Huber loss function, with a special focus on the modified Huber loss function (MHLF). Our exploration suggests that, compared to Bayesian optimal interval design (BOIN) and toxicity interval loss function (TILF), the approach based on the MHLF has been able to select correct doses with fewer average number of patients across the spectrum of dose escalation schemes such as those seen in clinical settings.

Keywords:

• Bayesian adaptive designs
• clinical trials
• continual reassessment methods
• maximum tolerated dose
• modified Huber loss function
• phase I trials
• toxicity interval loss function

Subject Classifications:

• 62B15
• 62C10
• 62C12
• 62F10
• 62F15
• 62K05
• 62L12

ACKNOWLEDGEMENTS

The authors are indebted to the Editor-in-Chief, the editorial staff, and the reviewers for their suggestions and constructive criticisms that improved the quality of this article.

DISCLOSURE

The authors have no conflicts of interest to report.