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.

项目总结/摘要 随机对照试验（RCT）是评价安全性和有效性的金标准方法。 癌症和神经紊乱等疾病的治疗方法。由于疾病杂- 疾病罕见或招募差异，可用于临床试验的患者有限， 使得药物开发成本高且耗时，试验失败率高。世纪 治愈法案呼吁监管机构和药物开发商考虑创新的临床试验 将传统临床试验与真实世界数据（RWD）连接起来的设计，以克服其中一些问题 局限性。来自RWD的外部对照（EC）已用于构建对照组， 最终获得监管决策者批准的实验性试验。然而，关注 关于RWD与RCT的有效性和可比性，限制了其在更广泛背景下的使用 到目前为止。选择偏倚、变量定义的差异和未测量的混杂可能导致 如果RWD与RCT整合，则治疗效应估计值存在偏倚，推断不正确。目标 在本项目中，重点关注在整合RWD时解决隐藏的偏见，以提高 临床试验我们将开发一个新的敏感性分析框架，用于外部现实世界的使用 控制以评估结果对隐藏偏倚的稳健性，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 ases.我们将积极向研究人员传播和翻译这些新方法 来自行业、学术和监管机构的信息，通过示例性应用程序、最先进的软件， 统计分析计划模板、资源丰富网站以及讲习班和辅导班。我们的终极目标- 其主要目标是促进在监管决策中更有力地使用真实世界的证据。