Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (e.g., the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (e.g., dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor, and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.

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