Department of Internal Medicine, University of South Florida, Tampa, Florida, USA.
Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA.
J Eval Clin Pract. 2021 Apr;27(2):246-255. doi: 10.1111/jep.13475. Epub 2020 Sep 11.
RATIONALE, AIMS, AND OBJECTIVES: Assessing the performance of diagnostic tests requires evaluation of the amount of diagnostic uncertainty a test reduces. Statistical measures, such as sensitivity and specificity, currently dominating the evidence-based medicine (EBM) and related fields, cannot explicitly measure this reduction in diagnostic uncertainty. Mutual information (MI), an information theory statistic, explicitly quantifies diagnostic uncertainty by measuring information gain before vs after diagnostic testing. In this paper, we propose the use of MI as a single measure to express diagnostic test performance and demonstrate how it can be used in the meta-analysis of diagnostic test studies.
We use two case studies from the literature to demonstrate the applicability of MI meta-analysis in assessing diagnostic performance. Meta-analysis of studies evaluating (a) ultrasonography (US) to detect endometrial cancer and (b) magnetic resonance angiography to detect arterial stenosis.
The results of MI meta-analyses are comparable to those of traditional statistical measures' meta-analyses. However, the results of MI are easier to understand as it relates directly to the extent of uncertainty a diagnostic test can reduce. For example, the US test, diagnosing endometrial cancer, is 40% specific and 94% sensitive. The combination of these values is difficult to interpret and may lead to inappropriate assessment (eg, one could favour the test due to its high sensitivity, ignoring its low specificity). In terms of MI, however, a single metric shows that the test reduces diagnostic uncertainty by 10%, which many users may consider small under most circumstances.
We have demonstrated the suitability of MI in assessing the performance of diagnostic tests, which can facilitate easier interpretation of the true utility of diagnostic tests. Similarly, to the guidance for interpretation of effect size of treatment interventions, we also propose the guidelines for interpretation of the utility of diagnostic tests based on the magnitude of reduction in diagnostic uncertainty.
原理、目的和目标：评估诊断测试的性能需要评估测试减少的诊断不确定性的程度。目前主导循证医学（EBM）和相关领域的统计措施，如敏感性和特异性，无法明确测量这种诊断不确定性的减少。互信息（MI），一种信息论统计量，通过测量诊断测试前后的信息增益，明确量化诊断不确定性。在本文中，我们提出使用 MI 作为单一指标来表示诊断测试性能，并展示如何在诊断测试研究的荟萃分析中使用它。
我们使用文献中的两个案例研究来演示 MI 荟萃分析在评估诊断性能中的适用性。评估（a）超声（US）检测子宫内膜癌和（b）磁共振血管造影检测动脉狭窄的研究的荟萃分析。
MI 荟萃分析的结果与传统统计措施荟萃分析的结果相当。然而，MI 的结果更容易理解，因为它直接关系到诊断测试可以减少的不确定性程度。例如，US 测试诊断子宫内膜癌的特异性为 40%，敏感性为 94%。这些值的组合难以解释，可能导致不当评估（例如，由于其高敏感性，有人可能会倾向于该测试，而忽略其低特异性）。然而，就 MI 而言，单一指标表明该测试减少了 10%的诊断不确定性，在大多数情况下，许多用户可能认为这是很小的。
我们已经证明了 MI 在评估诊断测试性能方面的适用性，这可以促进更容易理解诊断测试的真正效用。同样，为了指导治疗干预效果大小的解释，我们还根据减少诊断不确定性的程度提出了基于诊断测试效用解释的指南。
