Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Stat Med. 2022 Feb 10;41(3):433-448. doi: 10.1002/sim.9269. Epub 2021 Dec 3.
Diagnostic accuracy, a measure of diagnostic tests for correctly identifying patients with or without a target disease, plays an important role in evidence-based medicine. Diagnostic accuracy of a new test ideally should be evaluated by comparing to a gold standard; however, in many medical applications it may be invasive, costly, or even unethical to obtain a gold standard for particular diseases. When the accuracy of a new candidate test under evaluation is assessed by comparison to an imperfect reference test, bias is expected to occur and result in either overestimates or underestimates of its true accuracy. In addition, diagnostic test studies often involve repeated measurements of the same patient, such as the paired eyes or multiple teeth, and generally lead to correlated and clustered data. Using the conventional statistical methods to estimate diagnostic accuracy can be biased by ignoring the within-cluster correlations. Despite numerous statistical approaches have been proposed to tackle this problem, the methodology to deal with correlated and clustered data in the absence of a gold standard is limited. In this article, we propose a method based on the composite likelihood function to derive simple and intuitive closed-form solutions for estimates of diagnostic accuracy, in terms of sensitivity and specificity. Through simulation studies, we illustrate the relative advantages of the proposed method over the existing methods that simply treat an imperfect reference test as a gold standard in correlated and clustered data. Compared with the existing methods, the proposed method can reduce not only substantial bias, but also the computational burden. Moreover, to demonstrate the utility of this approach, we apply the proposed method to the study of National-Eye-Institute-funded Telemedicine Approaches to Evaluating of Acute-Phase Retinopathy of Prematurity (e-ROP), for estimating accuracies of both the ophthalmologist examination and the image evaluation.
诊断准确性是衡量诊断测试正确识别患有或不患有目标疾病的患者的指标，在循证医学中起着重要作用。新测试的诊断准确性理想情况下应通过与金标准进行比较来评估；然而，在许多医学应用中，为特定疾病获得金标准可能具有侵入性、昂贵甚至不道德。当通过与不完善的参考测试进行比较来评估新候选测试的准确性时，预计会出现偏差，并导致其真实准确性的高估或低估。此外，诊断测试研究通常涉及对同一患者进行多次测量，例如配对的眼睛或多颗牙齿，并且通常会导致相关和聚类数据。使用传统的统计方法来估计诊断准确性可能会因忽略簇内相关性而产生偏差。尽管已经提出了许多统计方法来解决这个问题，但在没有金标准的情况下处理相关和聚类数据的方法仍然有限。在本文中，我们提出了一种基于复合似然函数的方法，用于根据敏感性和特异性来推导诊断准确性的简单直观的闭式解。通过模拟研究，我们说明了与简单地将不完善的参考测试视为相关和聚类数据中的金标准的现有方法相比，该方法的相对优势。与现有方法相比，该方法不仅可以减少大量偏差，还可以减少计算负担。此外，为了展示该方法的实用性，我们将该方法应用于国家眼科研究所资助的远程医疗方法评估早产儿急性相视网膜病变 (e-ROP) 的研究中，以估计眼科医生检查和图像评估的准确性。
