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TSNP:一种免疫治疗的两阶段非参数 I/II 期临床试验设计。

TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy.

机构信息

Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA.

Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana, USA.

出版信息

Pharm Stat. 2021 Mar;20(2):282-296. doi: 10.1002/pst.2075. Epub 2020 Oct 6.

Abstract

We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity-efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.

摘要

我们开发了一种透明高效的两阶段非参数(TSNP)I/II 期临床试验设计,以确定免疫治疗的最佳生物学剂量(OBD)。我们提出了一种非参数方法,在毒性结果单调递增的约束下,推导出毒性-疗效联合反应概率的闭式估计。然后,这些估计用于测量每个剂量的免疫治疗的毒性-疗效特征,并指导剂量发现。设计的第一阶段旨在探索毒性特征。第二阶段旨在通过效用函数同时考虑毒性和疗效结果来找到 OBD,从而达到最佳的治疗效果。闭式估计和简洁的剂量发现算法使 TSNP 设计在实践中具有吸引力。模拟结果表明,TSNP 设计的运行特性优于现有的贝叶斯参数设计。我们提供了免费的用户友好型计算软件,以方便将建议的设计应用于真实试验。我们提供了全面的说明和示例,介绍了如何使用相关软件来实现所提出的设计。

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A Bayesian Phase I/II Trial Design for Immunotherapy.一种用于免疫疗法的贝叶斯 I/II 期试验设计。
J Am Stat Assoc. 2018;113(523):1016-1027. doi: 10.1080/01621459.2017.1383260. Epub 2018 Jun 28.

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