Collaborative Research: Empirical Likelihood Based Statistical Methods for Diagnostic Systems

合作研究：基于经验似然的诊断系统统计方法

• 批准号: 0603931
• 负责人: Xiao-Hua Zhou
• 金额: --
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0603931&HistoricalAwards=false
• 关键词: Collaborative; Research; Empirical; Likelihood; Based

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.

受试者工作特征（ROC）曲线方法是用于评估预测规则准确性的统计方法。它通常用于广泛的科学领域，如信号检测理论，医学成像，天气预报，反恐系统的真实的和虚假警报测试，以及诊断医学。为了评估皮肤镜、前列腺癌筛查试验和音频试验的诊断准确性，以及在ROC研究中找到易于处理和易于实施的复杂统计问题的解决方案，在本项目中，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，特别是对部分或全部ROC曲线下面积（AUC）以及ROC和AUC回归模型的统计推断。在本项目中，研究者从三个方向将传统的经验似然（EL）扩展到ROC分析的设置：（1）开发基于EL的ROC曲线下部分面积的半参数方法，（2）开发基于EL的ROC曲线下部分或全部面积的非参数方法，（3）开发基于EL的ROC和AUC回归模型的统计方法。 新方法有望比现有方法在评价竞争性诊断系统时更稳健、更准确。他们有潜在的更好的小样本性能比现有的方法。开发了一个软件，用于实际使用的新提出的方法进行ROC分析。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，我们身边的各种高科技诊断系统取得了巨大的进步。许多新的诊断系统已被用于揭示人的疾病、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。为了选择更准确的诊断系统以广泛使用，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，如在本项目中开发的ROC分析的半参数和非参数方法，将帮助诊断系统用户做出明智的选择，最可靠的诊断系统。从长远来看，这可能有助于降低保健费用。