Zhaohui Su, VP of Biostatistics at Ontada, shared a post on LinkedIn:
“Bayesian Optimal Interval (BOIN) designs are increasingly used in modern early-phase oncology trials. They provide a model-assisted and operationally straightforward approach to identifying the maximum tolerated dose (MTD) or the optimal biological dose (OBD), all while ensuring robust statistical performance.
BOIN includes designs for toxicity only, joint toxicity – efficacy decision-making, continuous or graded outcomes, delayed events, and even drug combinations. This unified framework allows teams to navigate complex scenarios with available software for easy implementation.
This paper by Revathi Ananthakrishnan and colleagues offers an insightful overview of the original BOIN design, its extensions, along with their advantages and limitations, and examples of applications. This is a helpful reference for anyone involved in designing innovative early-phase oncology studies.”
Title: An overview of the BOIN design and its current extensions for novel early-phase oncology trials
Authors: Revathi Ananthakrishnan, Ruitao Lin, Chunsheng He, Yanping Chen, Daniel Li, Michael LaValley
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