CLINICALLY RELEVANT USE OF PARTIAL ROC CURVES IN PERFORMANCE COMPARISON STUDIES

部分 ROC 曲线在性能比较研究中的临床相关应用

• 批准号: 8299493
• 负责人: Andriy Bandos
• 金额: $ 28.79万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8299493
• 关键词: Address; Area; Characteristics; Clinical; Data; Data Set; Development; Diagnostic; Diagnostic Procedure; Environment; Future; Guidelines; Investigation; Laboratories; Lead; Methodology; Methods; Modality; Performance; Positioning Attribute; Procedures; Publishing; ROC Curve; Receiver Operating Characteristics; Relative (related person); Research; Research Design; Research Personnel; Research Proposals; Sample Size; Specificity; System; Techniques; Testing; Time; base; clinical practice; clinically relevant; design; direct application; experience; improved; indexing; interest; novel strategies; research study; simulation; tool

DESCRIPTION (provided by applicant): The Receiver Operating Characteristic curve (ROC) is the most commonly used approach for assessing and comparing performance levels of diagnostic procedures that may involve the observer as an integral part of the system / practice. One of the more clinically relevant methods of ROC analysis is based on the part of the ROC curve which describes operating characteristics that are expected to be actually observable in clinical practice. A well known example of such an approach is the analysis based on partial area under the ROC curve (partial AUC) over the range of operating points with low false positive fraction (FPF). Although analyses based on a part of the ROC curve are frequently considered to be more relevant, these are not commonly employed due to several important issues. Two of these are the difficulty with a consistent definition of the actual range of interest and the potential loss of efficiency, namely higher likelihood of inconclusive results of a study or the need for a larger sample size for a future study. Our research proposal is primarily related to: 1) Defining the appropriate portion of interest of an ROC curve when estimated for a laboratory experiment performed outside of the actual clinical environment (given that the region of interest is known for the clinical ROC curve). Indeed the same decision threshold in a retrospective laboratory experiment is likely to have different operating characteristics (i.e. FPF and TPF) and therefore the direct application of the range of interest defined in terms of FPF is inappropriate and could lead to inconclusive and less, or totally irrelevant, results; 2) Empirical operating points in performance assessment studies typically span only the region of low FPF and therefore consideration of the entire ROC curve rather than the empirically supported part of the curve may not actually increase the statistical efficiency of comparisons of diagnostic modalities. The research we propose herein will help researchers use partial ROC analysis in a more relevant manner leading to more conclusive results, as well as provide guidelines on the potential statistical tradeoffs between partial and full ROC analyses in commonly encountered experimentally ascertained data, thereby addressing concerns about efficiency. Our proposed effort will provide researchers with three difference methods to define the clinically relevant portion on a laboratory-estimated ROC curve. We will also develop an approach for comparison of partial AUCs by combining strengths of both parametric and nonparametric approaches. This will provide a more interpretable and statistically efficient technique for this purpose. Last, we will investigate the relative efficiency of the partial and full AUC in different types of data commonly encountered experimentally where the empirical operating points span only the range of low FPF, and develop an approach that combines practical relevance of the partial AUC with statistical stability of the full AUC. All investigated analyses will be evaluated using extensive simulations, as well as an assessment of the possible effects, if any, of the proposed analyses on conclusions of previously performed and published large ROC type studies.

描述（由申请人提供）：受试者工作特征曲线（ROC）是评估和比较诊断程序性能水平的最常用方法，可能涉及观察者作为系统/实践的组成部分。ROC分析的临床相关方法之一是基于ROC曲线的一部分，该曲线描述了预期在临床实践中实际可观察到的操作特征。这种方法的一个众所周知的例子是基于在具有低假阳性分数（FPF）的操作点范围内的ROC曲线下的部分面积（部分AUC）的分析。虽然基于ROC曲线的一部分的分析通常被认为是更相关的，但由于几个重要的问题，这些分析并不常用。其中两个问题是难以对实际关注范围作出一致的定义，以及潜在的效率损失，即研究结果不确定的可能性更大，或需要为未来的研究提供更大的样本量。我们的研究建议主要涉及：1）当为实际临床环境之外进行的实验室实验进行估计时，定义ROC曲线的适当感兴趣部分（假设临床ROC曲线的感兴趣区域已知）。事实上，在回顾性实验室实验中，相同的决策阈值可能具有不同的操作特性（即FPF和TPF），因此直接应用FPF定义的利益范围是不适当的，可能导致不确定和较少或完全无关的结果;（二）性能评估研究中的经验操作点通常仅跨越低FPF区域，因此考虑整个ROC曲线，而不是该曲线实际上可能不会增加诊断模式比较的统计效率。我们在此提出的研究将帮助研究人员以更相关的方式使用部分ROC分析，从而获得更结论性的结果，并为常见的实验确定数据中部分和完整ROC分析之间的潜在统计权衡提供指导，从而解决对效率的担忧。我们提出的努力将为研究人员提供三种不同的方法来定义实验室估计的ROC曲线上的临床相关部分。我们还将通过结合参数和非参数方法的优势，开发一种比较部分AUC的方法。这将为此目的提供一种更可解释和统计上更有效的技术。最后，我们将研究在实验中经常遇到的不同类型的数据中部分和完整AUC的相对效率，其中经验操作点仅跨越低FPF的范围，并开发一种将部分AUC的实际相关性与完整AUC的统计稳定性相结合的方法。将使用广泛的模拟对所有研究分析进行评价，并评估拟定分析对先前进行和发表的大型ROC类型研究结论的可能影响（如有）。