Improved DBM ROC Methods for Diagnostic Radiology

该建议解决了诊断放射学研究中的一个基本方法学问题： 评价成像方式的诊断性能。接收器工作特性(ROC) 分析是一种公认的程序，用来衡量成像设备使医生能够检测到 疾病。Dorfman，Berbaum和Metz(1992)(DBM)发展了一种统计方法来分析ROC 有多个读者的研究，同时考虑到读者样本和病例样本的差异。DBM 方法学是评估新成像系统诊断性能的最广泛使用的方法。 不幸的是，DBM方法的实用性和有效性是有限的：DBM只处理一个狭窄的 一系列实验设计，不能处理丢失的数据。DBM结论的可靠性 当患病的患者比未患病的患者多得多或少得多时，可能会出现问题。 DBM不能用有序回归来拟合ROC曲线，制约了ROC模型的进一步发展 防止采用更先进的统计技术。DBM不能使用患者信息 提高统计能力。DBM只比较诊断系统的诊断准确性，而不是 给患者和社会带来的好处和成本。我们的目标是通过扩展DBM来消除这些限制 方法的适用范围，并通过提高其有效性。 具体目标是：(1)建立一个更通用的DBM MRMC分析模型 处理计算机辅助诊断实验的广义最小二乘法，多元协变量 效果、数据缺失、设计分读者或案例、读者阅读不同案例；(2)发展 一种选项，用于确定推断对方差-协方差估计的可变性的敏感性 当案件数量较少时，当案件数量适中但比例较高时 正常到异常的情况接近于1或0；(3)提供有效且易于使用的序数 在我们的主要DBM MRMC方法中用于ROC曲线拟合的回归选项，提供熟悉的和 可解释的ROC风格的结果；(4)扩展DBM MRMC以包括特定于疾病的患者水平协变量 发现协变量如何影响准确性，从而通过解释获得更多的统计能力 结果的差异；以及(5)开发DBM MRMC方法以进行成本/效益分析，以关注 对ROC曲线临床相关部分的诊断系统进行比较，并提供定量的 卫生政策决策的基础。完成这些目标将提高小学教育的有效性。 用于评估新成像系统诊断性能的方法。这将导致更好的 放射学研究，这将提高卫生保健的质量和降低成本。

This proposal addresses a fundamental methodological problem in diagnostic radiology research: evaluation of diagnostic performance of imaging modalities. Receiver operating characteristic (ROC) analysis is an accepted procedure for measuring how well imaging modalities allow physicians to detect disease. Dorfman, Berbaum and Metz (1992) (DBM) developed a statistical methodology to analyze ROC studies with multiple readers, taking into account both reader-sample and case-sample variation. DBM methodology is the most widely used method to assess diagnostic performance of new imaging systems. Unfortunately, the usefulness and effectivenessof DBM method is limited: DBM only handles a narrow range of experimental designs and cannot cope with missing data. The soundness of DBM conclusions may be in question when there are many more or many less diseased patients than non-diseased patients. DBM does not fit ROC curves using ordinal regression, restraining further development of ROC models and preventing the incorporation of more advanced statistical techniques. DBM cannot use patient information to improve statistical power. DBM only compares diagnostic systems on accuracy of diagnosis, not on the benefits and costs for patients and society. Our goal is to remove these limitations by extending DBM method's range of application and by improving its effectiveness. The specific aims are:(1) formulate a more general model for DBM MRMC analysis using the estimated generalized least squares method to cope with computer-aided diagnosis experiments, multiple covariate effects, missing data, designs split on readers or cases, and readers reading different cases; (2) develop an option that determines the sensitivity of inferencesto the variability of the variance-covariance estimates when the number of cases is small, and when the number of cases is moderate but the proportion of normal to abnormal cases is close to one or zero; (3) provide a validated and easy-to-use ordinal regression option for ROC curve fitting within our major DBM MRMC method that offers familiar and interpretable ROC-style results; (4) extend DBM MRMC to include a disease-specific patient-level covariate to discover how the covariate affects accuracy and thereby gain more statistical power by explaining variation in outcomes; and (5) develop DBM MRMC methodology for cost/benefit analysis to focus the comparison of diagnostic systems on clinically relevant parts of ROC curves and provide a quantitative basis for health policy decisions. Completing these aims will enhance the effectiveness of the primary method used to assess the diagnostic performance of new imaging systems. This will lead to better radiology research, which will raise the quality and lower the cost of health care.