TY - JOUR
T1 - BOIN
T2 - A novel Bayesian design platform to accelerate early phase brain tumor clinical trials
AU - Yuan, Ying
AU - Wu, Jing
AU - Gilbert, Mark R.
N1 - Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology and the European Association of Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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 (eg, 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 (eg, 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.
AB - 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 (eg, 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 (eg, 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.
KW - Bayesian adaptive designs
KW - Bayesian optimal interval design
KW - biological optimal dose
KW - maximum tolerated dose
KW - model-assisted design
UR - http://www.scopus.com/inward/record.url?scp=85121124139&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121124139&partnerID=8YFLogxK
U2 - 10.1093/nop/npab035
DO - 10.1093/nop/npab035
M3 - Article
C2 - 34777832
AN - SCOPUS:85121124139
SN - 2054-2577
VL - 8
SP - 627
EP - 638
JO - Neuro-Oncology Practice
JF - Neuro-Oncology Practice
IS - 6
ER -