ROC Curve Methodology

实验室正在不断开发用于早期发现和预防慢性和急性疾病的新的和复杂的生物标志物。受试者工作特征(ROC)曲线在生物医学研究中用于评估生物标志物在区分有无疾病个体方面的有效性。ROC曲线下面积是最常用的诊断测试有效性的总体衡量标准。有大量文献对ROC曲线下面积在健康和疾病受试者测量结果分布的各种假设下的统计推断进行了研究。 我们的研究重点是中华民国方法论的发展，特别是在以下问题上： 1.在高成本化验的情况下，根据较少数量的混合样本分析结果被发现是有用的。我们开发了设计方法和统计工具来评估这些生物标志物。我们将在这方面扩大我们的研究，特别是我们将评估在混合样本下的最优截止点的估计以及我们在先前工作中所做的假设的稳健性。 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已变得更加常见。在这样的设置中有几个相关的来源。我们在正态假设下考虑这类研究的设计和分析问题。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率。Su和Liu(1993)导出了使接收器工作特性(ROC)曲线下的面积最大化的线性组合。然而，这些线性组合对于高特异度的敏感性可能较低。我们进一步研究了Su和Liu(1993)的线性组合的性能，并寻找表现良好的高特异度的替代方案。 4.中华民国研究的进一步发展正在进行中，例如对早期发现疾病的生物标志物进行顺序评估。 5.许多生物标志物数据存在选择偏差。推荐偏差是筛查测试数据中最常见的选择偏差类型。我们开发了纠正ROC曲线下区域的转诊偏差的方法。

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.