Improved DBM ROC Methods for Diagnostic Radiology

改进的诊断放射学 DBM ROC 方法

• 批准号: 7598948
• 负责人: Kevin Staley Berbaum
• 金额: $ 59.95万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7598948
• 关键词: Accounting; Address; Adoption; Affect; Architecture; Caring; Computer software; Computer-Assisted Diagnosis; Cost-Benefit Analysis; Costs and Benefits; Data; Data Set; Development; Diagnostic; Diagnostic radiologic examination; Disease; Effectiveness; Equilibrium; Evaluation Research; Experimental Designs; Freedom; Goals; Health Care Costs; Health Policy; Image; Lead; Least-Squares Analysis; Lesion; Location; Measures; Methodology; Methods; Modality; Modeling; Outcome; Patients; Performance; Physicians; Population; Prevalence; Procedures; Radiology Specialty; Reader; Reading; Receiver Operating Characteristics; Relative (related person); Research; Research Personnel; Sampling; Sensitivity and Specificity; Societies; Specific qualifier value; Structure; System; Techniques; Variant; base; clinically relevant; coping; cost; design; diagnostic accuracy; imaging modality; improved; indexing; innovation; interest; novel; open source; prevent; programs; research study; software development; statistics; user-friendly

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.

描述（由申请人提供）：该提案解决了诊断放射学研究中的一个基本方法学问题：评价成像模式的诊断性能。受试者工作特征（ROC）分析是一种公认的程序，用于测量成像模式如何使医生能够检测疾病。Dorfman、Berbaum和梅斯（1992）（DBM）开发了一种统计方法来分析多位读者的ROC研究，同时考虑读者样本和病例样本的差异。DBM方法是评估新成像系统诊断性能的最广泛使用的方法。然而，DBM方法的实用性和有效性是有限的：DBM只能处理范围很窄的实验设计，不能科普缺失数据。DBM结论的合理性可能会受到质疑，当有更多或更少的患病患者比非患病患者。DBM不使用有序回归拟合ROC曲线，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无法使用患者信息来提高统计功效。DBM只比较诊断系统的诊断准确性，而不是对患者和社会的利益和成本。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）使用估计的广义最小二乘法来制定用于DBM MRMC分析的更通用的模型，以科普计算机辅助诊断实验、多协变量效应、缺失数据、对读取者或病例的设计分割以及读取者阅读不同的病例;（2）开发一个选项，当案例数量较少时，该选项确定推断对方差-协方差估计的变异性的敏感性，（3）在我们的主要DBM MRMC方法中为ROC曲线拟合提供了一个经过验证且易于使用的有序回归选项，该方法提供了熟悉且可解释的ROC风格结果;（4）扩展DBM MRMC以包括疾病特异性患者水平协变量，以发现协变量如何影响准确性，从而通过解释结果的变化获得更大的统计功效;（5）发展DBM MRMC成本/效益分析方法，将诊断系统的比较集中在ROC曲线的临床相关部分，为卫生决策提供定量依据。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。