Methods to improve efficiency and robustness of clinical trials using information from real-world data with hidden bias

使用来自真实世界数据的信息（具有隐藏偏差）提高临床试验的效率和稳健性的方法

• 批准号: 10797500
• 负责人: Xiaofei Wang
• 金额: $ 85.21万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797500
• 关键词: Methods; improve; efficiency; robustness; clinical

PROJECT SUMMARY/ABSTRACT Randomized controlled trials (RCTs) are the gold-standard method of evaluating the safety and effi- cacy of treatments for diseases, such as cancer and neurological disorders. Due to disease hetero- geneity, disease rarity, or enrollment disparities, there are limited patients available for clinical trials, making drug development costly and time-consuming with trials failing at a high rate. The 21st Century Cures Act has called for regulatory agencies and drug developers to consider innovative clinical trial designs that bridge conventional clinical trials with real-world data (RWD) to overcome some of these limitations. External controls (ECs) from RWD have been used to construct the comparator arm in con- firmatory trials that eventually received approval from regulatory decision-makers. However, concerns regarding the validity and comparability of RWD with RCTs have limited their use in a broader context thus far. Selection bias, differences in variable definitions, and unmeasured confounding can lead to biased treatment effect estimates and incorrect inference if RWD are integrated with RCTs. The aims in this project focus on addressing hidden biases when integrating RWD to improve the efficiency of clinical trials. We will develop a novel sensitivity analysis framework for the use of external real-world controls to assess the robustness of results to hidden biases, as well as robust and efficient analysis methods that selectively borrow and adjust for data discrepancies to mitigate the impact of hidden bi- ases. We will actively engage in dissemination and translation of these new methods to researchers from industry, academic and regulatory agencies through exemplary applications, state-of-art software, statistical analysis plan template, resourceful website, and workshops and tutorial sessions. Our ulti- mate goal is to facilitate more robust use of real-world evidence in regulatory decision-making.

项目概要/摘要 随机对照试验（RCT）是评估安全性和有效性的金标准方法 治疗癌症和神经系统疾病等疾病的有效性。由于疾病异质性 基因、疾病稀有性或入组差异，可供临床试验的患者有限， 药物开发成本高昂且耗时，试验失败率很高。 21世纪 《治愈法案》呼吁监管机构和药物开发商考虑创新临床试验 将传统临床试验与真实世界数据 (RWD) 连接起来的设计，以克服其中的一些问题 限制。 RWD 的外部控制（EC）已用于构造比较器臂 最终获得监管决策者批准的验证性试验。然而，担忧 关于 RWD 与 RCT 的有效性和可比性限制了它们在更广泛的背景下的使用 到目前为止。选择偏差、变量定义的差异以及未测量的混杂因素都可能导致 如果将 RWD 与 RCT 结合起来，治疗效果估计会出现偏差和推断不正确。目标 在这个项目中，重点是解决集成 RWD 时隐藏的偏差，以提高效率 临床试验。我们将开发一种新颖的敏感性分析框架，以利用外部现实世界 评估结果对隐藏偏差的稳健性的控制，以及稳健和有效的分析 有选择地借用和调整数据差异的方法，以减轻隐藏的双向影响 情况。我们将积极向研究人员传播和转化这些新方法 来自行业、学术和监管机构的示范性应用程序、最先进的软件、 统计分析计划模板、资源丰富的网站以及研讨会和辅导课程。我们的终极 mate 的目标是促进在监管决策中更强有力地使用现实世界的证据。