Accelerating biomarker development through novel statistical methods for analyzing phase III/IV studies

项目摘要/摘要 各个实验室发现的众多候选癌症生物标记物都很有说服力。 加强精准医疗实践的潜力。然而，这是一个漫长而具有挑战性的过程-- 往往以失败告终--在使用这些生物标志物之前，没有严格地开发和验证它们 在临床实践中。特别是，第三、第四和第五阶段的生物标记物验证研究费用昂贵，而且时间- 进行费时费力；必须仔细设计和分析这些研究，并最大限度地 EFfi充分利用收集到的标本。由我们在生物标记物开发方面的合作推动 对于癌症的早期发现，这项提议寻求开发尖端的统计工具来分析阶段 III和IV生物标记物的研究，以加快生物标记物的开发进程。这些方法 在目标1中提出的目标是选择主要终点和推理程序，以适应 在IV期试验中评估fi筛查结果时可能会出现过度诊断。AIM中提出的方法 2能够在第三阶段生物标志物开发中将第四阶段样品与第三阶段样品相结合。 目标3中提出的方法集成了来自不同研究队列的信息(它们在 筛选模式和资格标准)评估生物标记物临床应用的设计参数时 审判。 我们的统计方法将立即应用于分析来自两个癌症应用程序的数据- TIONS：i)新发糖尿病(NOD)队列研究和早期发现倡议(EDI)研究 胰腺癌的早期发现，以及II)fiVe低剂量CT(LDCT)筛查队列和前列腺癌， 肺癌、结直肠癌和卵巢癌筛查(PLCO)试验用于肺癌筛查。此外， 开发的方法学将在其他III和IV期癌症生物标志物研究中得到更广泛的应用 这将对推进国家情报院早期检测研究网络S目前的优先事项具有重要意义 设计生物标记物临床实用试验。本提案中开发的所有统计程序和算法 将向公众免费提供。

Project Summary/Abstract The multitude of candidate cancer biomarkers being discovered across various laboratories hold great potential to enhance the practice of precision medicine. However, it is a long and challenging process – often culminating in failure – to rigorously develop and validate these biomarkers before they can be used in clinical practice. In particular, phase III, IV, and V biomarker validation studies are expensive and time- consuming to conduct; it is essential to carefully design and analyze these studies and to make the most efficient use of the specimens collected. Motivated by our collaborative work on biomarker development for cancer early detection, this proposal seeks to develop cutting-edge statistical tools for analyzing phase III and IV biomarker studies in order to accelerate the biomarker development process. The methods proposed in Aim 1 target the selection of primary endpoints and inference procedures to accommodate potential overdiagnosis when assessing screening efficacy in phase IV trials. The methods proposed in Aim 2 enable the combination of phase IV samples with phase III samples in phase III biomarker development. The methods proposed in Aim 3 integrate information from heterogeneous study cohorts (which differ in screening modalities and eligibility criteria) when estimating design parameters for biomarker clinical utility trials. Our statistical methods will have immediate applications to analysis of data from two cancer applica- tions: i) the New Onset Diabetes (NOD) Cohort study and the Early Detection Initiative (EDI) study for pancreatic cancer early detection, and ii) five low-dose CT (LDCT) screening cohorts and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) trial for lung cancer screening. Moreover, the developed methodology will have broader application in other phase III and IV cancer biomarker studies and will be valuable for advancing the NCI Early Detection Research Network (EDRN)'s current priority in designing biomarker clinical utility trials. All statistical programs and algorithms developed in this proposal will be made freely available to the public.