Bayesian optimal interval designs for phase I clinical trials

TLDR

Phase I trials prioritize treating patients effectively while minimizing exposure to subtherapeutic or overly toxic doses. The study proposes Bayesian optimal interval (BOIN) designs to identify the maximum tolerated dose and reduce inappropriate dose assignments. The BOIN design, a Bayesian interval approach, is applied to two cancer clinical trials. The BOIN design demonstrates superior finite and large‑sample properties, easy implementation akin to the “3+3” design, comparable MTD selection to the continual reassessment method, and a substantially lower risk of subtherapeutic or overly toxic dose assignments.

Abstract

In phase I trials, effectively treating patients and minimizing the chance of exposing them to subtherapeutic and overly toxic doses are clinicians' top priority. Motived by this practical consideration, we propose Bayesian optimal interval (BOIN) designs to find the maximum tolerated dose and to minimize the probability of inappropriate dose assignments for patients. We show, both theoretically and numerically, that the BOIN design not only has superior finite and large sample properties but also can be easily implemented in a simple way similar to the traditional '3+3' design. Compared with the well-known continual reassessment method, the BOIN design yields comparable average performance to select the maximum tolerated dose but has a substantially lower risk of assigning patients to subtherapeutic and overly toxic doses. We apply the BOIN design to two cancer clinical trials.