Collaborative Research: Empirical Likelihood Based Statistical Methods for Diagnostic Systems

受试者工作特征（ROC）曲线方法学是评价预测规则准确性的统计方法学。它广泛应用于信号检测理论、医学成像、天气预报、反恐系统真假报警检测、诊断医学等科学领域。为了评估皮肤镜、前列腺癌筛查试验和音频测试的诊断准确性，以及为ROC研究中复杂的统计问题找到易于处理和易于实施的解决方案的需要，本项目研究人员开发了新的半参数和非参数统计方法用于ROC分析，特别是对ROC曲线下部分区域或全区域（AUC）的统计推断以及ROC和AUC回归模型。在本项目中，研究者将传统的经验似然（EL）扩展到ROC分析的设置中，分为三个方向：(1)针对ROC曲线下的部分区域开发基于EL的半参数方法；(2)针对ROC曲线下的部分或全部区域开发基于EL的非参数方法；(3)针对ROC和AUC回归模型开发基于EL的统计方法。新方法有望比现有方法在评估竞争性诊断系统方面更加稳健和准确。它们可能比现有方法具有更好的小样本性能。为实际使用新提出的ROC分析方法，开发了一个软件。该项目开发的方法是对现有医学诊断测试的极大补充，适用于广泛的科学领域。在过去的二十年里，我们周围的各种高科技诊断系统取得了巨大的进步。许多新的诊断系统被用于揭示人类的疾病、核电站的故障、制造产品的缺陷、飞机的碰撞过程、恐怖分子的威胁活动等。然而，诊断系统的成本可能很高。为了选择更准确的诊断系统来广泛使用，重要的是开发适当的统计方法来评估竞争系统的诊断准确性。使用有吸引力的统计方法，如本项目开发的ROC分析的半参数和非参数方法，将帮助诊断系统用户做出最可靠的诊断系统的明智选择。从长远来看，这可能有助于降低保健费用。

The receiver operating characteristic (ROC) curve methodology is the statistical methodology for assessment of the accuracy of prediction rules. It is commonly used in a wide range of scientific fields such as signal detection theory, medical imaging, weather forecasting, real and false alarms testing for anti-terrorist system, and diagnostic medicine. Motivated by assessing diagnostic accuracy of Dermoscope, prostate cancer screening test, and audio test and the need to find tractable and easily implemented solution to complex statistical problems in ROC studies, in this project the investigators develop new semi-parametric and nonparametric statistical methods for ROC analysis, particularly on the statistical inferences for the partial areas or the full areas under the ROC curves (AUC) and ROC and AUC regression models. In this project, the investigators extend the traditional empirical likelihood (EL) to the setting of ROC analysis in three directions: (1) to develop EL-based semi-parametric methods for the partial areas under the ROC curves, (2) to develop EL-based nonparametric methods for the partial or full areas under the ROC curves, and (3) to develop EL-based statistical methods for ROC and AUC regression models. The new methods are expected to be more robust and more accurate than the existing methods in evaluation of competing diagnostic systems. They have potentially better small sample performances than the existing methods. A software is developed for the actual use of the newly proposed methods for ROC analysis. The methods developed from this project are great addition to existing medical diagnostic testing and applicable to a wide range of scientific fields. High-tech diagnostic systems of various kinds around us have been made tremendous progress in the last two decades. Many new diagnostic systems have been used to reveal diseases in people, malfunctions in nuclear power plants, flaws in manufactured products, collision courses of aircraft, threatening activities of terrorists, etc. However, costs of diagnostic systems can be high. To select more accurate diagnostic system for widespread use, it is important to develop appropriate statistical methods for evaluating the diagnostic accuracy of competing systems. The use of attractive statistical methods like the semi-parametric and nonparametric methods for ROC analysis developed in this project is going to help diagnostic systems users make informed choices of the most reliable diagnostic systems. This may contribute to the reduction of health care costs in the long run.