A Bayesian adaptive phase 1 design to determine the optimal dose and schedule of an adoptive T-cell therapy in a mixed patient population
A Bayesian adaptive phase 1 design to determine the optimal dose and schedule of an adoptive T-cell therapy in a mixed patient population
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DOI：
10.1016/j.cct.2016.04.004
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发表时间：
2016-05-01
影响因子：
2.2
通讯作者：
Connor, Jason T.
中科院分区：
文献类型：
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作者：
Quintana, Melanie;Li, Daniel H.;Connor, Jason T.
我们提出了一种新的贝叶斯自适应第一阶段设计，以确定混合患者人群中过继T细胞治疗的最佳剂量方案。我们的设计是由一项B细胞非霍奇金淋巴瘤试验激发的，该试验评估了多种疾病亚型内的多种给药方案。效用分数是根据安全性和有效性效用函数计算的，并用于指导剂量递增决策。我们汇集了跨疾病亚型的安全数据，并使用单一剂量-毒性模型，同时使用分级剂量-反应模型在疾病亚型之间共享疗效信息。此外，当多个方案开放登记时，应用自适应随机方法动态地将患者分配到一个方案。我们通过一个模拟的试验例子来说明这种研究设计，并用模拟研究来研究它的运行特性。(C)2016 Elsevier Inc.保留所有权利。
We present a novel Bayesian adaptive phase 1 design to determine the optimal dosing regimen for an adoptive T-cell therapy in a mixed patient population. Our design is motivated by a B-cell Non-Hodgkin Lymphoma trial evaluating multiple dosing regimens within multiple disease subtypes. A utility score is calculated from both safety and efficacy utility functions and used to guide dose-escalation decisions. We pool safety data across disease subtypes and use a single dose-toxicity model while sharing efficacy information between disease subtypes using a hierarchical dose-response model. In addition, an adaptive randomization approach is applied to dynamically assign patients to a regimen when more than one regimen is open for enrollment. We illustrate this study design through a simulated trial example, and we investigate the operating characteristics using simulation studies. (C) 2016 Elsevier Inc. All rights reserved.