ROC Curve Methodology

用于早期检测和预防慢性和急性疾病的新的和复杂的生物标志物正在实验室环境中不断开发。受试者工作特征（ROC）曲线用于生物医学研究，以评估生物标志物在区分患有和没有疾病的个体中的有效性。ROC曲线下面积是诊断测试有效性的最常用的总体测量。在健康和疾病受试者的测量分布的各种假设下，有大量关于ROC曲线下面积的统计推断的文献。 我们的研究重点是ROC方法论的发展，特别是在以下问题： 1.在高成本测定的背景下，已经发现基于较少数量的合并样本分析结果是有用的。我们开发了设计方法和统计工具来评估这些生物标志物。我们将扩大我们在这方面的研究，特别是我们将评估合并样本下的最佳截点的估计，以及我们在以前的工作中所做的假设的鲁棒性。 2.在疾病诊断中，对来自同一患者的多个样本进行诊断测试变得越来越普遍。在这种情况下，有几个相关性的来源。我们认为这些研究的设计和分析问题下的正态性假设。我们正在将这些方法扩展到非参数设置。 3.我们还考虑联合多种生物标志物以提高诊断准确性。Su和Liu（1993）推导出使受试者工作特征（ROC）曲线下面积最大化的线性组合。然而，这些线性组合对于高特异性可能具有低灵敏度。我们进一步研究了Su和Liu（1993）线性组合的性能，并寻求在高特异性上表现良好的替代方案。 4. ROC研究的进一步发展正在进行中，例如用于疾病早期检测的生物标志物的连续评估。 5.许多生物标志物数据受到选择偏倚的影响。参考偏倚是筛选试验数据中最常见的选择偏倚类型。我们开发了方法来纠正推荐偏倚的ROC曲线下面积。

New and sophisticated biomarkers for the early detection and prevention of chronic and acute diseases are constantly being developed in laboratory settings. Receiver Operating Characteristics (ROC) curves are used in biomedical research to evaluate the effectiveness of biomarkers in distinguishing individuals with and without a disease. The area under the ROC curve is the most commonly used overall measure of diagnostic tests' effectiveness There is substantial literature on statistical inference on the area under the ROC curve under various assumptions on the distributions of the measurements from healthy and disease subjects. We focus our research on ROC methodology development; especially in the following problems: 1.In the context of high cost assays, analyzing the results based on a smaller number of pooled specimens has been found to be useful. We developed design methods and statistical tools to evaluate these biomarkers. We will expand our research in this area, especially we will evaluate the estimation of the optimal cut point under pooled samples and also the robustness of the assumptions we have made in our previous work. 2.In the diagnosis of diseases, it has become more common for the diagnostic test to be performed on multiple samples that come from the same patient. There are several sources of correlation in such settings. We consider the design and analysis issues of such studies under normality assumption. We are expanding these methods to a nonparametric setting. 3.We also consider combining multiple biomarkers to improve diagnostic accuracy. Su and Liu (1993) derived the linear combinations that maximize the area under the receiver operating characteristic (ROC) curve. These linear combinations, however, may have low sensitivity for high specificity. We further investigate the performance of Su and Liu's (1993) linear combination and seek alternatives that perform well on high specificity. 4.Further developments in ROC research are underway such as sequential evaluation of biomarkers for the early detection of diseases. 5.Many biomarkers data is subject to selection bias. Referral bias is the most common type of selection bias in screening tests data. We developed methods to correct for referral bias for the area under the ROC curve.