Applying novel statistical approaches to develop a decision framework for hybrid randomized controlled trial designs which combine internal control arms with patients' data from real-world data source

项目摘要捕获的真实的世界数据（RWD）的技术进步 从医疗保健来源，使一代不断扩大的机构， 关于医疗产品使用的证据（RWE）。这些新的证据来源 可以通过减少样本量和/或缩短 审判持续时间，但尚未得到充分利用。RWD的一个应用程序可以 对临床试验的进行产生重大影响的是将这些数据用作外部数据， 对照特别关注的 是混合随机对照试验设计， 用来自真实世界数据源的患者水平数据补充内部对照组。 D 内部控制和外部控制之间的不相似性有可能产生负面影响。 影响试验（例如，降低功效，夸大I类错误率）， 随机对照试验。贝叶斯方法，自适应调整的影响， 对试验数据分析的外部控制有助于缓解这些问题， 平衡与这类复杂试验设计相关的风险和回报。 通过我们与哥伦比亚大学生物统计系的合作， 在北卡罗来纳州，我们正在开发一种适应性借款方法， 特别适用于事件发生时间分析的受试者特定贴现参数。 拟议的项目将使我们能够扩大与欧盟的合作， 新的决策框架（模拟工具，包括R-包， 计算可行的SAS宏，以及一组研究设计考虑因素） 当使用混合临床试验时， 监管决策。我们将重点关注以下目标：（1）评估 混合动力设计及其操作特性，当与顺序 监测并可能使用自适应随机化，（2）评估可能的 当应用于疾病时， 不同的治疗领域，包括罕见疾病和（3）R- 支持研究设计模拟和提供关于 使用软件包来审查FDA的工作人员。 在计算可行的情况下，我们 将开发SAS宏，以及使这些公开。实现我们 我们将利用来自已完成的临床试验、RWD来源和模拟的数据 问题研究我们计划举办跨学术界和工业界的年度小型会议， 探讨如何利用拟议设计的运行特性， 制定监管决策，并制定敏感性分析的推荐清单 这将支持基于混合研究设计的监管申报。我们 总体目标是使我们开发的决策框架公开可用。

PROJECT ABSTRACT Technological advances in real world data (RWD) captured from healthcare sources have enabled generation of an expanding body of real-world evidence (RWE) on the use of medical products. These novel sources of evidence can increase efficiencies of clinical trials by reducing sample size and/or shortening trials duration, but have yet to be fully utilized. One application of RWD that could significantly impact the conduct of clinical trials is the use of these data as external controls. Of special interest are hybrid randomized controlled trial designs, which supplement internal control arms with patients’ level data from real-word data sources. D issimilarity between internal and external controls has the potential to negatively impact the trial (e.g., decrease power, inflate type I error rate) as compared to randomized control trials. Bayesian methods which adaptively adjust the influence of external controls on the analysis of the trial data can help to mitigate these issues and balance the risks and rewards associated with this type of complex trial designs. Through our collaboration with the Department of Biostatistics at the University of North Carolina (UNC) we are developing an adaptive borrowing approach with subject-specific discounting parameters specifically suited for time-to-event analyses. The proposed project would allow us to expand the UNC collaboration and develop a novel decision framework (simulation tools, including R-Packages and where computationally feasible SAS macros, and a set of study design considerations) allowing reliable application of our method when using hybrid clinical trials for regulatory decision making. We would focus on the following aims: (1) evaluation of the hybrid designs and their operating characteristics, when combined with sequential monitoring and possibly use of adaptive randomization, (2) assessment of possible extensions of the method beyond time-to-event settings when applied to diseases in different therapeutic areas, including rare diseases and (3) development of R- Packages supporting study design simulations and offering training workshops on the use of the packages to review staff at the FDA. Where computationally feasible, we will develop SAS macros as well and make these publicly available. To achieve our aims, we will utilize data from completed clinical trials, RWD sources and simulation studies. We plan to hold annual mini-conferences cross academia and industry to explore how operating characteristics of the proposed designs could be utilized for regulatory decision making and develop a recommended list of sensitivity analyses that would support regulatory submissions based on hybrid study designs. Our overarching objective is to make our developed decision framework publicly available.