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A crossed random effects modeling approach for estimating diagnostic accuracy from ordinal ratings without a gold standard

机译:交叉随机效应建模方法,可根据无金标准的有序等级评估诊断准确性

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摘要

In diagnostic studies without a gold standard, the assumption on the dependence structure of the multiple tests or raters plays an important role in model performance. In case of binary disease status, both conditional independence and crossed random effects structure have been proposed and their performance investigated. Less attention has been paid to the situation where the true disease status is ordinal. In this paper, we propose crossed subject-specific and rater-specific random effects to account for the dependence structure and assess the robustness of the proposed model to misspecification in the random effects distributions. We applied the models to data from the Physician Reliability Study, which focuses on assessing the diagnostic accuracy in a population of raters for the staging of endometriosis, a gynecological disorder in women. Using this new methodology, we estimate the probability of a correct classification and show that regional experts can more easily classify the intermediate stage than resident physicians.
机译:在没有黄金标准的诊断研究中,对多个测试或评估者的依存结构的假设在模型性能中起着重要作用。对于二元疾病状态,已经提出了条件独立性和交叉随机效应结构,并研究了它们的性能。对真正疾病状况为序的情况的关注较少。在本文中,我们提出了交叉的,针对特定对象和针对评估者的随机效应,以说明依赖性结构,并评估了所提出的模型对随机效应分布中错误指定的鲁棒性。我们将这些模型应用于“医师可靠性研究”中的数据,该研究重点在于评估评估者人群中子宫内膜异位症(妇女的妇科疾病)分期的诊断准确性。使用这种新方法,我们估计正确分类的可能性,并显示区域专家比驻地医师更容易对中间阶段进行分类。

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