Journal of Clinical Trials
Open Access

Abstract

Bayesian Interval Based Designs for Phase I Dose-Escalation Trials: A Case Study in Oncology

Tim Clark, Ayon Mukherjee*, Peter Lichtlen and James Sweeney

The objective of phase I dose-escalation clinical trials has generally been to determine the Maximum Tolerated Dose (MTD). However, with the advent of molecular targeted therapies this approach has changed, as dose limiting toxicities are less frequently observed. For this reason, the concept of Optimal Biological Dose (OBD) has been developed, which considers efficacy and toxicity. Several Bayesian model-assisted designs have been proposed to target the MTD more accurately and/or the OBD compared to traditional rule-based approaches such as the 3+3 design. These include the Bayesian Optimal Interval (BOIN) and the BOIN phase I/II (BOIN12) design. The BOIN design targets the MTD, while the BOIN12, which takes both efficacy and toxicity into account in decisions to escalate/de-escalate the dose, targets the OBD. In this article we use a real-life case study to compare the BOIN and the BOIN12 designs under different scenarios and showcase how each of the designs perform when the compound under investigation has a benign toxicity profile. We argue that both efficacy and toxicity should be taken into consideration when designing early-phase oncology studies.

Published Date: 2024-05-24; Received Date: 2024-04-24