Considerations for master protocols using external controls.

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Jie Chen, Xiaoyun Nicole Li, Chengxing Cindy Lu, Sammy Yuan, Godwin Yung, Jingjing Ye, Hong Tian, Jianchang Lin
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引用次数: 0

Abstract

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

使用外部控制的主协议的注意事项。
在肿瘤临床试验中,主方案的使用越来越多,因为它能有效加快癌症药物的开发,并能灵活适应多个子研究。根据研究目标和设计的不同,主方案试验可以是篮子试验、伞形试验、平台试验或其他任何形式的试验,在这些试验中,多个研究产品和/或亚群在一个方案下进行研究。主方案可以使用外部数据和证据(如外部对照)来估计治疗效果,从而进一步提高主方案试验的效率。本文概述了不同类型的外部对照及其在主方案中使用时的独特之处。本文还讨论了使用外部对照的主方案中的一些关键注意事项,包括构建估计指标、评估适合使用的真实世界数据以及不同类型主方案的注意事项。还讨论了常规随机对照试验与使用外部对照的主方案之间的异同。介绍了基于目标学习的因果关系路线图,其中包括三个关键步骤:(1) 确定与研究目标因果估计相一致的目标统计估计值,(2) 使用有效估计器估计目标统计估计值及其不确定性,(3) 通过执行敏感性分析评估因果假设对研究结论的影响。本文讨论了使用外部控制的主方案的两个示例,以说明其优点以及在因果效应估计方面可能的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
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