TY - JOUR
T1 - A Bayesian phase I/II platform design for co-developing drug combination therapies for multiple indications
AU - Mu, Rongji
AU - Xu, Jin
AU - Tang, Rui
AU - Kopetz, Scott
AU - Yuan, Ying
N1 - Publisher Copyright:
© 2021 John Wiley & Sons Ltd.
PY - 2022/1/30
Y1 - 2022/1/30
N2 - There is a growing trend to combine a new targeted or immunotherapy agent with the cancer-specific standard of care to treat different types of cancers. We propose a master-protocol-based, Bayesian phase I/II platform design to co-develop combination (BPCC) therapies in multiple indications. Under the BPCC design, only a single master protocol is needed, and the combined drug is evaluated in different indications in a concurrent or staggered fashion. For each indication, we jointly model dose-toxicity and -efficacy relationships and employ Bayesian hierarchical models to borrow information across them for more efficient indication-specific decision-making. To account for the characteristic of targeted or immunotherapy agents that their efficacy may not monotonically increase with the dose, and often plateau at high doses, we use the utility to quantify the risk-benefit tradeoff of the treatment. At each interim, we update the toxicity and efficacy model, as well as the estimate of the utility, based on the observed data across indications to inform the indication-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each indication. Simulation study shows that the BPCC design has desirable operating characteristics, and that it provides an efficient approach to accelerate the development of combination therapies.
AB - There is a growing trend to combine a new targeted or immunotherapy agent with the cancer-specific standard of care to treat different types of cancers. We propose a master-protocol-based, Bayesian phase I/II platform design to co-develop combination (BPCC) therapies in multiple indications. Under the BPCC design, only a single master protocol is needed, and the combined drug is evaluated in different indications in a concurrent or staggered fashion. For each indication, we jointly model dose-toxicity and -efficacy relationships and employ Bayesian hierarchical models to borrow information across them for more efficient indication-specific decision-making. To account for the characteristic of targeted or immunotherapy agents that their efficacy may not monotonically increase with the dose, and often plateau at high doses, we use the utility to quantify the risk-benefit tradeoff of the treatment. At each interim, we update the toxicity and efficacy model, as well as the estimate of the utility, based on the observed data across indications to inform the indication-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each indication. Simulation study shows that the BPCC design has desirable operating characteristics, and that it provides an efficient approach to accelerate the development of combination therapies.
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U2 - 10.1002/sim.9242
DO - 10.1002/sim.9242
M3 - Article
C2 - 34730248
AN - SCOPUS:85118500011
SN - 0277-6715
VL - 41
SP - 374
EP - 389
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 2
ER -