A Bayesian adaptive dose-finding algorithm for balancing individual- and population-level ethics in Phase I clinical trials

作者：Kim Steven B^*; Gillen Daniel L

来源：Sequential Analysis, 2016, 35(4): 423-439.

DOI：10.1080/07474946.2016.1238250

• Adaptive design
• Individual- and population-level ethics
• Phase I clinical trials

Multiple Bayesian adaptive designs have been proposed for Phase I clinical trials since the continual reassessment method (CRM) was proposed by O'Quigley et al. (1990). Focused on dose-finding in cancer tudies, the CRM seeks to allocate new patients to an estimated maximum tolerable dose (MTD). Later, Whitehead and Brunier (1995) applied Bayesian decision theory to maximize statistical information for the MTD when allocating new patients. The two allocation rules reflect conflicting perspectives. The CRM emphasizes individual-level ethics, whereas the method of Whitehead and Brunier (1995) emphasizes population-level ethics. In the design of a Phase I clinical trial to investigate Ilyperthermic intraperitoneal chemotherapy (HIPEC) we sought to compromise the two perspectives. To this end, we propose a novel dose allocation design referred to as the balanced information gain method. We first decompose the loss function used by Whitehead and Brunier and then modify it with a tuning parameter that allows a trialist to differentially weigh individual- and population-level ethics based on their particular clinical setting. Simulation studies show that the proposed design provides a reasonable compromise between the distribution of the estimated MTD and;the distribution of the number of observed adverse events per trial when compared to the two existing methods.

• 出版日期2016