Semiparametric Statistical Inferences for ROC Curves and Surfaces under Density Ratio Models

密度比模型下ROC曲线和曲面的半参数统计推断

• 批准号: 0603873
• 负责人: Biao Zhang
• 金额: $ 7.32万
• 依托单位: University of Toledo
• 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: 2006-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0603873&HistoricalAwards=false
• 关键词: Semiparametric; Statistical; Inferences; ROC; Curves

Receiver operating characteristic (ROC) curves are commonly used to measure the accuracy of diagnostic tests in discriminating disease and nondiasease. The investigator studies four important statistical applications of the density ratio model in semiparametric ROC curve and surface analyses. Analogous to the nonparametric kernel-based ROC curve analysis, the investigator studies the semiparametric kernel estimators of the ROC curve and its area on the basis of the maximum semiparametric likelihood estimators of the underlying distribution functions under a two-sample density ratio model. Furthermore, the investigator proposes three approaches for comparing the accuracy of two diagnostic tests with paired or unpaired data. Moreover, the investigator studies the maximum semiparametric likelihood estimator of the best combination of two or more diagnostic tests by directly modeling the likelihood ratio function under a density ratio model. In addition, as a generalization of semiparametric ROC curve analysis to semiparametric ROC surface analysis, the investigator studies maximum semiparametric likelihood estimation of the ROC surface and its volume in the context of multiple-class diagnostic problems by extending the two-sample density ratio model to a multiple-sample density ratio model. As an alternative to the Cox proportional hazards model and the Lehmann alternative model, the natural connection between the semiparametric density ratio model and the logistic regression model has enhanced its recent popularity. It is anticipated that statistical inferences based on the density ratio model would be more robust than a fully parametric approach and would be more efficient than a fully nonparametric approach.An important role of research in diagnostic medicine is to estimate and compare the accuracies of diagnostic tests, enabling one to determine if a new diagnostic test is as good as the standard reference test or if an inexpensive test has an acceptable inferiority in sensitivity or specificity. In clinical practice, several medical diagnostic tests are often available, yet they may not be perfect in the sense that no single test is sufficiently sensitive and specific on its own for the purpose of population disease screening. One approach to improving the performance of screening is to combine multiple diagnostic tests so as to obtain an optimal composite diagnostic test with higher sensitivity that detects presence of the disease more accurately. The proposed activity has important applications in the evaluation of medical diagnostic tests, all of which are beneficial to practitioners in biological and medical communities. In particular, the proposed activity provides a more robust and efficient statistical methodology for assessing the accuracy of diagnostic tests used in the practice of medicine, thereby enhancing the statistical evaluation of medical diagnostic tests for classification and prediction. Thus, the proposed activity would be widely applicable in the field of diagnostic medicine and other related interdisciplinary problems. In addition, the proposed research activity has greater educational impacts on statistics teaching and learning, in that much of the proposed material can be utilized in classroom teaching and incorporated into textbooks on semiparametric models for master and doctoral students in statistics and biostatistics.

受试者工作特征（ROC）曲线通常用于衡量诊断试验在区分疾病和非疾病方面的准确性。研究了密度比模型在半参数ROC曲线和曲面分析中的四个重要统计应用。类似于非参数核ROC曲线分析，研究者在两样本密度比模型下，基于潜在分布函数的最大半参数似然估计，研究了ROC曲线及其面积的半参数核估计。此外，研究人员提出了三种方法来比较具有配对或非配对数据的两种诊断测试的准确性。此外，研究者研究的最大半参数似然估计的最佳组合的两个或两个以上的诊断测试直接建模密度比模型下的似然比函数。此外，作为半参数ROC曲线分析半参数ROC表面分析的推广，研究者通过将两样本密度比模型扩展到多样本密度比模型，研究了多类诊断问题中ROC表面及其体积的最大半参数似然估计。作为考克斯比例风险模型和Lehmann替代模型的替代模型，半参数密度比模型和逻辑回归模型之间的天然联系增强了其最近的流行。预计基于密度比模型的统计推断将比完全参数方法更稳健，并且将比完全非参数方法更有效。诊断医学研究的一个重要作用是估计和比较诊断测试的准确性，使得人们能够确定新的诊断测试是否与标准参考测试一样好，或者便宜的测试是否在灵敏度或特异性方面具有可接受的劣性。在临床实践中，通常有几种医学诊断测试，但它们可能并不完美，因为没有一种测试本身就足够敏感和特异，用于人群疾病筛查。一种提高筛查性能的方法是将联合收割机多种诊断测试组合，以获得具有更高灵敏度的最佳复合诊断测试，其更准确地检测疾病的存在。拟议的活动在医学诊断测试的评价方面具有重要的应用，所有这些都有利于生物和医学界的从业人员。特别是，拟议的活动提供了一种更强大、更有效的统计方法，用于评估医学实践中使用的诊断测试的准确性，从而增强对医学诊断测试的统计评估，以进行分类和预测。因此，拟议的活动将广泛适用于诊断医学领域和其他相关的跨学科问题。此外，拟议的研究活动对统计教学和学习有更大的教育影响，因为许多拟议的材料可以用于课堂教学，并纳入统计和生物统计硕士和博士生的半参数模型教科书。