CLINICALLY RELEVANT USE OF PARTIAL ROC CURVES IN PERFORMANCE COMPARISON STUDIES

描述（由申请人提供）：接受者工作特征曲线（ROC）是最常用的方法，用于评估和比较可能涉及观察者作为系统/实践的组成部分的诊断程序的性能水平。ROC分析中与临床更相关的方法之一是基于ROC曲线中描述在临床实践中实际可观察到的操作特征的部分。这种方法的一个众所周知的例子是基于低假阳性分数（FPF）工作点范围内ROC曲线下部分面积（partial 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，这些分析通常不被采用。其中两个是难以对实际的兴趣范围进行一致的定义，以及潜在的效率损失，即研究结果不确定的可能性更高，或者需要为未来的研究提供更大的样本量。我们的研究计划主要涉及：1)在对实际临床环境之外进行的实验室实验进行估计时，定义ROC曲线的适当兴趣部分（考虑到临床ROC曲线已知的兴趣区域）。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征（即FPF和TPF），因此直接应用FPF定义的兴趣范围是不合适的，可能导致不确定的、较少的或完全无关的结果；2)绩效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曲线而不是经验支持的曲线部分实际上可能不会提高诊断方式比较的统计效率。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偏ROC分析，从而获得更结论性的结果，并为在常见的实验确定的数据中使用偏ROC分析和全ROC分析之间的潜在统计权衡提供指导，从而解决效率问题。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。我们还将开发一种方法，通过结合参数和非参数方法的优势来比较部分auc。这将为这一目的提供一种更可解释和统计效率更高的技术。最后，我们将研究在实验中经常遇到的不同类型数据中部分和完全AUC的相对效率，其中经验工作点仅跨越低FPF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。所有被调查的分析都将使用广泛的模拟进行评估，以及评估可能的影响，如果有的话，建议的分析对以前进行和发表的大型ROC型研究的结论的影响。

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. PUBLIC HEALTH RELEVANCE: The design and analysis of performance assessment studies based on partial ROC curves is frequently more relevant than comparisons of complete ROC curves. However, many analytical and practical issues need to be resolved if a partial ROC approach is to be commonly used for this purpose. We propose to investigate a number of the more important issues in this regard and to develop new tools that will enable clinically relevant use of partial ROC analyses for this purpose and at the same time increase the possibility of conclusive results while decreasing sample size requirements for partial ROC type study designs.