Statistical Methods and Validation Analyses for the Integration of External Data in Clinical Trials

项目概要/摘要 我们的研究将侧重于使用外部数据，包括以前的临床研究和现实世界 数据集，用于II期和III期肿瘤学试验的设计和分析。例如，我们认为， 包括基于试验和外部数据的提前停止决策的设计 患者级数据。 外部数据集有可能改善未来单一项目的最终分析和中期决策， 手臂和随机临床试验。它们还可以加速新疗法的开发， 减少需要入组临床研究的患者数量， 持续时间然而，使用外部信息来分析临床试验目前是零星的。 事实上，整合外部患者水平的信息来测试新的治疗方法可以增加 实验性治疗评估中的偏倚风险。有效利用外部数据， 临床研究的设计和分析需要充分的统计方法， 验证分析，与标准统计数据相比， 单组和随机试验设计的计划。 我们将开发新的设计，在未来的试验中使用外部数据。我们将使用数据集的集合 在前列腺癌、胶质母细胞瘤和肺癌中，包括患者水平的结局和预后 变量这些收集对于在临床研究中有效使用外部数据是必要的。我们将 然后介绍和应用验证方法来评估统计设计，使用疾病特异性 数据收集，包括临床试验和真实的世界数据。我们将提供的验证总结 生产，将量化试验设计的效率和外部数据整合的风险， 例如，与未测量的混杂因素或预后变量的测量误差相关 和结果。

Project Summary/Abstract Our research will focus on the use of external data, including previous clinical studies and real-world datasets, in the design and analysis of phase II and III oncology trials. We consider, for example, designs that include early stopping decisions based on data generated from the trial and external patient-level data. External datasets have the potential to improve final analyses and interim decisions of future single- arm and randomized clinical trials. They can also accelerate the development of new treatments, by reducing the number of patients that need to be enrolled in clinical studies and therefore their duration. However the use of external information to analyze clinical trials is currently sporadic. Indeed, the integration of external patient-level information to test new treatments can increase the risk of bias in the evaluation of experimental treatments. An effective use of external data in the design and analysis of clinical studies requires both, adequate statistical methodologies, and validation analyses, to quantify risks and potential efficiency gains compared to standard statistical plans of single-arm and randomized trial designs. We will develop novel designs to use external data in future trials. We will use collections of datasets in prostate cancer, glioblastoma, and lung cancer, including patient-level outcomes and prognostic variables. These collections are necessary to effectively use external data in clinical studies. We will then introduce and apply validation methods to evaluate statistical designs using disease-specific data collections, inclusive of clinical trials and real world data. The validation summaries that we will produce, will quantify the efficiency of trial designs and the risks of the integration of external data, associated for example, to unmeasured confounders or measurement errors on prognostic variables and outcome.