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Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers
Biometrics ( IF 1.9 ) Pub Date : 2021-01-13 , DOI: 10.1111/biom.13421
Yeonhee Park 1 , Suyu Liu 2 , Peter F Thall 2 , Ying Yuan 2
Affiliation  

Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients is sensitive to it and thus may benefit from it therapeutically. In practice, it is often assumed based on preclinical data that a treatment-sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between preclinical settings and human biology, however, data from patients treated with a new targeted agent often show that one or both of these assumptions are false. This paper provides a Bayesian randomized group sequential enrichment design that compares an experimental treatment to a control based on survival time and uses early response as an ancillary outcome to assist with adaptive variable selection and enrichment. Initially, the design enrolls patients under broad eligibility criteria. At each interim decision, submodels for regression of response and survival time on a baseline covariate vector and treatment are fit; variable selection is used to identify a covariate subvector that characterizes treatment-sensitive patients and determines a personalized benefit index, and comparative superiority and futility decisions are made. Enrollment of each cohort is restricted to the most recent adaptively identified treatment-sensitive patients. Group sequential decision cutoffs are calibrated to control overall type I error and account for the adaptive enrollment restriction. The design provides a basis for precision medicine by identifying a treatment-sensitive subpopulation, if it exists, and determining whether the experimental treatment is superior to the control in that subpopulation. A simulation study shows that the proposed design reliably identifies a sensitive subpopulation, yields much higher generalized power compared to several existing enrichment designs and a conventional all-comers group sequential design, and is robust.

中文翻译:

基于基线生物标志物响应和生存时间自适应回归的贝叶斯组序贯富集设计

精准医学依赖于这样一种想法,即对于特定的靶向药物,只有一小部分患者对其敏感,因此可能会从治疗中受益。在实践中,通常基于临床前数据假设治疗敏感的亚群是已知的,而且该药剂在该亚群中实质上是有效的。然而,由于临床前环境和人类生物学之间的重要差异,来自接受新靶向药物治疗的患者的数据通常表明这些假设中的一个或两个都是错误的。本文提供了贝叶斯随机组序贯富集设计,该设计将实验治疗与基于生存时间的对照进行比较,并使用早期反应作为辅助结果来协助自适应变量选择和富集。最初,该设计根据广泛的资格标准招募患者。在每个临时决定中,对基线协变量向量和治疗的反应和生存时间回归的子模型进行拟合;变量选择用于识别表征治疗敏感患者的协变量子向量并确定个性化的受益指数,并做出比较优劣的决策。每个队列的注册仅限于最近自适应识别的治疗敏感患者。校准组顺序决策截止值以控制总体 I 型错误并考虑自适应注册限制。该设计通过识别治疗敏感的亚群(如果存在)为精准医学提供了基础,并确定在该亚群中实验治疗是否优于对照。一项模拟研究表明,与现有的几种富集设计和传统的全来者组顺序设计相比,所提出的设计能够可靠地识别敏感亚群,产生更高的泛化能力,并且是稳健的。
更新日期:2021-01-13
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