Improved DBM ROC Methods for Diagnostic Radiology

描述(由申请人提供)：这项建议解决了诊断放射学研究中的一个基本方法学问题：评估成像模式的诊断性能。受试者工作特性(ROC)分析是一种公认的程序，用于测量成像设备允许医生检测疾病的程度。Dorfman，Berbaum和Metz(1992)(DBM)发展了一种统计方法来分析多个读者的ROC研究，同时考虑了读者样本和病例样本的变化。DBM方法是评估新型成像系统诊断性能的最广泛的方法。不幸的是，DBM方法的实用性和有效性是有限的：DBM只处理很小范围的实验设计，不能处理丢失的数据。当患病患者比非患病患者多得多或少得多时，DBM结论的可靠性可能会受到质疑。DBM不能用顺序回归来拟合ROC曲线，这限制了ROC模型的进一步发展，并阻止了更先进的统计技术的结合。DBM不能使用患者信息来提高统计能力。DBM只比较诊断系统的准确性，而不是对患者和社会的好处和成本进行比较。我们的目标是通过扩大DBM方法的应用范围和提高其有效性来消除这些限制。具体目标是：(1)利用估计广义最小二乘法建立DBM-MRMC分析的更一般模型，以处理计算机辅助诊断实验、多个协变量效应、数据缺失、按读者或案例分开设计以及读者阅读不同案例；(2)当案例数量较少时，当案例数量适中但正常与异常案例的比例接近1或0时，开发一种确定推断对方差-协方差估计变异性的敏感度的选项；(3)在我们主要的DBM MRMC方法中为ROC曲线拟合提供一个有效且易于使用的顺序回归选项，提供熟悉和可解释的ROC风格的结果；(4)扩展DBM MRMC以包括特定疾病的患者水平的协变量，以发现该协变量如何影响准确性，从而通过解释结果的差异获得更多的统计能力；以及(5)开发用于成本/收益分析的DBM MRMC方法，将诊断系统的比较集中在ROC曲线的临床相关部分，并为卫生政策决策提供量化基础。完成这些目标将提高用于评估新成像系统诊断性能的主要方法的有效性。这将导致更好的放射学研究，这将提高医疗保健的质量和降低成本。

DESCRIPTION (provided by applicant): 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 effectiveness of 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 inferences to 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.