Discovery, Evaluation and Clinical Decision Making Based on Non-Monotone Biomarkers for the Early Detection of Disease

该项目将开发新的统计方法，以促进发现被当前统计方法遗漏并被天真地忽视为缺乏信息的生物标记物，尽管它们可能表现出很大的歧视能力。我们的工作将集中在使用基于血液的生物标记物进行疾病早期检测的高通量技术上。对于给定的化验，这些技术允许我们拥有多个生物标记物，这些生物标记物通常基于传统测量方法进行排名，例如接收器操作特征曲线(AUC)下的面积或根据临床设置在给定的特异性/敏感性水平上的敏感性/特异性。传统标准认为，较高的生物标记物水平增加了对疾病存在的怀疑(反之亦然)。然而，在一些情况下，这种单向性被严重违反。在大量的候选者中拥有这样的标记物通常会导致研究人员根据AUC、敏感度、特异度或部分AUC(PAUC)对它们进行排名，这取决于临床环境，并只关注排名靠前的候选者。所有这些传统的衡量标准都不能揭示出一份有前途的生物标志物的适当排名名单。因此，这些生物标记物及其行为不能被临床医生和生物学家进一步探索/验证，仅仅是因为统计分析没有使这些标记物对他们可用。因此，这些潜在的优秀生物标记物被当前的统计技术遗漏了。该项目旨在开发允许违反上述方向性的新指标。我们将提供一个完整的框架，允许发现新的生物标记物(无论其方向性如何)，评估这些生物标记物，评估它们的临床用途，并建立一个基于截止日期的决策过程。

This project will develop new statistical methodologies to facilitate the discovery of biomarkers that are missed by current statistical methods and naively ignored as uninformative, in spite of the great discriminatory ability they may exhibit. Our work will focus on high-throughput technologies for the early detection of disease using blood-based biomarkers. For a given assay, these technologies allow us to have multiple biomarkers that are commonly ranked based on traditional measures such as the area under the receiver operating characteristic curve (AUC) or sensitivity/specificity at a given level of specificity/sensitivity depending on the clinical setting. Traditional criteria assume that a higher biomarker level increases the suspicion of the presence of the disease (or vice versa). There are cases, however, in which this single-directionality is severely violated. Having such markers in a large pool of candidates typically leads investigators to rank them by AUC, sensitivity, specificity, or partial AUC (pAUC), depending on the clinical setting, and to focus on only the top candidates. All these traditional metrics cannot reveal an appropriately ranked list of promising biomarkers. As a result, these biomarkers and their behavior cannot be further explored/validated by clinicians and biologists, simply because the statistical analysis does not make such markers available to them. Thus, these potentially excellent biomarkers are missed with current statistical techniques. This projects aims to develop new metrics that allow violations of the aforementioned directionality. We will provide a full framework that allows for discovery of new biomarkers (regardless of their directionality), evaluation of these biomarkers, assessment of their clinical utility and construction of a cutoff-based decision-making process.