Use of likelihood ratios for comparisons of binary diagnostic tests: Underlying ROC curves
Use of likelihood ratios for comparisons of binary diagnostic tests: Underlying ROC curves
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DOI：
10.1118/1.3503849
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发表时间：
2010-11-01
期刊：
影响因子：
3.8
通讯作者：
Gur, David
中科院分区：
文献类型：
--
作者：
Bandos, Andriy I.;Rockette, Howard E.;Gur, David
用途：当比较来自两个诊断系统的二元测试结果时，“灵敏度”和“特异性”的优越性也意味着所有常规汇总指标和局部基础受试者工作特征（ROC）曲线的差异。然而，当两个二元测试之一具有较高的灵敏度和较低的特异性（或反之亦然），其性能水平的比较是不平凡的，使用不同的汇总指标可能会导致矛盾的结论。一个常用的方法，是免费的主观性与总结指数是基于基本的ROC曲线的比较，需要使用多类别的规模，无论是自然的或实验强加的评级数据的收集。然而，可靠估计ROC曲线的数据往往是不可用的。本文的目的是开发一种使用“诊断似然比”的方法，即“阳性”或“阴性”反应的似然比，在缺乏可靠评级数据的情况下或在主要关注相对二元特征时，对潜在ROC曲线和相关区域进行简单推断。方法：对于与潜在曲线相关的推断，作者利用真实潜在ROC曲线的近似假设来描述这些曲线必须不同以及曲线具有不同面积的条件。对于二元特征是主要关注的场景，作者使用“机会性能”的特征来证明推导出的条件提供了强有力的证据，证明一种二元测试与另一种二元测试相比具有优越性。通过将这些派生条件的假设的真实似然比的两个二元诊断测试进行比较，作者使一个简单的统计过程相应的inferences.Results：作者得出简单的代数和图形方法描述的条件优越性的两个诊断测试之一，其二进制特性，相关的ROC曲线，或曲线下面积。图形区域对于识别两个系统之间的潜在差异非常有用，然后必须进行统计测试。简单的统计检验可以用公知的方法进行，用于比较诊断似然比。所开发的方法为一些更难以分析的情况提供了解决方案，其中诊断测试在灵敏度和特异性方面没有表现出一致的差异。此外，由此产生的推论不矛盾的结论，可以得到使用传统的和合理定义的摘要index.Conclusions：当二进制诊断测试是主要的兴趣，所提出的方法提供了一个客观的和强大的方法比较两个二进制诊断测试。该方法的显著优点是，当一种测试具有较高的灵敏度但较低的特异性时，它能够进行客观分析，同时确保与基于其他合理和广泛接受的汇总指标的研究结论一致。对于真正的多类别诊断测试，所提出的方法可以帮助基于二进制数据得出诊断测试之一的劣效性，从而可能节省进行更昂贵的多类别ROC研究的需要。(C)2010年美国医学物理学家协会。[DOI：10.1118/1.3503849]
Purpose: When comparing binary test results from two diagnostic systems, superiority in both "sensitivity" and "specificity" also implies differences in all conventional summary indices and locally in the underlying receiver operating characteristics (ROC) curves. However, when one of the two binary tests has higher sensitivity and lower specificity (or vice versa), comparisons of their performance levels are nontrivial and the use of different summary indices may lead to contradictory conclusions. A frequently used approach that is free of subjectivity associated with summary indices is based on the comparison of the underlying ROC curves that requires the collection of rating data using multicategory scales, whether natural or experimentally imposed. However, data for reliable estimation of ROC curves are frequently unavailable. The purpose of this article is to develop an approach of using "diagnostic likelihood ratios," namely, likelihood ratios of "positive" or "negative" responses, to make simple inferences regarding the underlying ROC curves and associated areas in the absence of reliable rating data or regarding the relative binary characteristics, when these are of primary interest.Methods: For inferences related to underlying curves, the authors exploit the assumption of concavity of the true underlying ROC curve to describe conditions under which these curves have to be different and under which the curves have different areas. For scenarios when the binary characteristics are of primary interest, the authors use characteristics of "chance performance" to demonstrate that the derived conditions provide strong evidence of superiority of one binary test as compared to another. By relating these derived conditions to hypotheses about the true likelihood ratios of two binary diagnostic tests being compared, the authors enable a straightforward statistical procedure for the corresponding inferences.Results: The authors derived simple algebraic and graphical methods for describing the conditions for superiority of one of two diagnostic tests with respect to their binary characteristics, the underlying ROC curves, or the areas under the curves. The graphical regions are useful for identifying potential differences between two systems, which then have to be tested statistically. The simple statistical tests can be performed with well known methods for comparison of diagnostic likelihood ratios. The developed approach offers a solution for some of the more difficult to analyze scenarios, where diagnostic tests do not demonstrate concordant differences in terms of both sensitivity and specificity. In addition, the resulting inferences do not contradict the conclusions that can be obtained using conventional and reasonably defined summary indices.Conclusions: When binary diagnostic tests are of primary interest, the proposed approach offers an objective and powerful method for comparing two binary diagnostic tests. The significant advantage of this method is that it enables objective analyses when one test has higher sensitivity but lower specificity, while ensuring agreement with study conclusions based on other reasonable and widely acceptable summary indices. For truly multicategory diagnostic tests, the proposed method can help in concluding inferiority of one of the diagnostic tests based on binary data, thereby potentially saving the need for conducting a more expensive multicategory ROC study. (C) 2010 American Association of Physicists in Medicine. [DOI: 10.1118/1.3503849]