Statistical Methods in Diagnostic Medicine

描述(由申请人提供)：诊断性测试有两个目的：(1)提供有关患者病情的可靠信息；(2)影响医生管理患者的计划。只有当医生知道如何解释它时，一项测试才能达到这些目的。这些信息是通过评估测试的诊断准确性而需要的，诊断准确性是测试区分疾病存在或不存在的能力。当诊断测试的响应是连续的时，其诊断准确性最好地由接收器工作特性(ROC)曲线来表示。目前应用最广泛的带有协变量的ROC曲线估计方法是参数或半参数方法。然而，估计的ROC曲线可能对连接函数的参数假设非常敏感。最后，目前的ROC研究大多采用固定样本设计，效率不高。虽然在ROC研究中使用成组序贯设计有许多很好的理由，但由于缺乏有效的方法学来进行ROC研究中的GSD，成组序贯设计在ROC研究中的应用并不广泛。在这个方案中，我们将建立具有协变量的ROC曲线的非参数回归模型和在数据单调变换下不变的估计方法。我们还将利用协变量和估计方法建立ROC曲线部分或全部区域的非参数回归模型。最后，我们将利用非参数估计的ROC曲线为比较ROC研究开发通用的成组序贯设计。

DESCRIPTION (provided by applicant): A diagnostic test has two purposes: (1) to provide reliable information about the patient's condition and (2) to influence the physician's plan for managing the patient. A test can serve these purposes only if the physicians know how to interpret it. This information is required through an assessment of the test's diagnostic accuracy, which is the ability of a test to discriminate between the presence or absence of the disease. When the response of a diagnostic test is continuous, its diagnostic accuracy is best represented by the receiver operating characteristic (ROC) curve. Currently most widely used methods for estimating ROC curves with covariates are parametric or semi-parametric. However, estimated ROC curves can be very sensitive to the parametric assumption on the link function. Finally, most current ROC studies employ fixed sample designs, which are not efficient. Although there are many good reasons for the use of group sequential designs in ROC studies, the group sequential design has not been used widely in ROC studies because of the lack of available methodology for conducting the GSD in ROC studies. In this proposal we will develop non-parametric regression models for ROC curves with covariates and estimation methods that are invariant under the monotone transformation of data. We will also develop nonparametric regression models for partial or full regions of ROC curves with covariates and estimation methods. Finally, we will develop general group sequential designs for comparative ROC studies using nonparametrically estimated ROC curves.