Bayesian Model Averaging Continual Reassessment Method in Phase I Clinical Trials

Abstract

The continual reassessment method (CRM) is a popular dose-finding design for phase I clinical trials. This method requires that practitioners prespecify the toxicity probability at each dose. Such prespecification can be arbitrary, and different specifications of toxicity probabilities may lead to very different design properties. To overcome the arbitrariness and further enhance the robustness of the design, we propose using multiple parallel CRM models, each with a different set of prespecified toxicity probabilities. In the Bayesian paradigm, we assign a discrete probability mass to each CRM model as the prior model probability. The posterior probabilities of toxicity can be estimated by the Bayesian model averaging (BMA) approach. Dose escalation or deescalation is determined by comparing the target toxicity rate and the BMA estimates of the dose toxicity probabilities. We examine the properties of the BMA-CRM approach through extensive simulation studies, and also compare this new method and its variants with the original CRM. The results demonstrate that our BMA-CRM is competitive and robust, and eliminates the arbitrariness of the prespecification of toxicity probabilities.

Keywords:  :

• Adaptive design
• Bayesian inference
• Maximum tolerated dose
• Model selection
• Posterior model probability
• Robustness
• Toxicity probability