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

项目总结/摘要 在各个实验室发现的众多候选癌症生物标志物具有很大的意义。 提高精准医疗实践的潜力。然而，这是一个漫长而充满挑战的过程- 这些生物标志物在使用之前， 在临床实践中。特别是，III期、IV期和V期生物标志物验证研究昂贵且耗时- 进行消耗;必须仔细设计和分析这些研究，并充分利用 有效利用收集到的标本。受我们在生物标志物开发方面的合作工作的启发 对于癌症的早期检测，该提案寻求开发尖端的统计工具，用于分析阶段， III和IV生物标志物研究，以加速生物标志物开发过程。的方法 目标1中提出的目标是选择主要终点和推断程序，以适应 在IV期试验中评估筛查有效性时可能存在过度诊断。Aim中提出的方法 2使得能够在III期生物标志物开发中将IV期样品与III期样品组合。 目标3中提出的方法整合了来自异质研究队列的信息（这些队列在以下方面不同）： 筛选方式和合格性标准）时，估计生物标志物临床效用的设计参数 审判 我们的统计方法将立即应用于两个癌症应用程序的数据分析- i）新发糖尿病（NOD）队列研究和早期检测倡议（EDI）研究， 胰腺癌早期检测，和ii）五个低剂量CT（LDCT）筛查队列和前列腺， 肺癌、结直肠癌和卵巢癌筛查（PLCO）试验用于肺癌筛查。而且 开发的方法将在其他III期和IV期癌症生物标志物研究中具有更广泛的应用 并将有助于推进NCI早期探测研究网络（EDRN）目前的优先事项， 设计生物标志物临床效用试验。本提案中开发的所有统计程序和算法 将免费提供给公众。

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.