Methods of assessing categorical agreement between correlated screening tests in clinical studies.
Methods of assessing categorical agreement between correlated screening tests in clinical studies.
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DOI：
10.1080/02664763.2020.1777394
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发表时间：
2021
影响因子：
1.5
通讯作者：
Nelson KP
中科院分区：
文献类型：
--
作者：
Zhou TJ;Raza S;Nelson KP
乳腺成像和其他筛查测试的进步促使研究评估和比较专家对现有筛查测试和新筛查测试的评级之间的一致性。在临床环境中，医学专家对筛查测试结果（例如乳房X光检查）进行主观评估。专家评级之间的一致性是通过评估者之间的一致性或关联性来评估的。然而，当研究由许多专家组成、数据不平衡或专家评分之间存在依赖性时，传统的衡量标准（例如 Cohen 和 Fleiss 的 kappas）就无法应用或可能表现不佳。在这里，我们评估现有方法的性能，包括最近开发的总结措施，用于评估患者接受两次筛查程序时专家的二元评级和序数评级之间的一致性。还描述了评估同一专家重复测量之间的一致性的方法。我们介绍了三项大规模临床筛选研究的应用。这些协议措施的特性通过模拟研究来说明。一般来说，基于模型的方法比其他方法具有多种优势，包括灵活合并各种测量尺度（即二元或序数）、大量专家和患者、稀疏数据以及对潜在疾病患病率的稳健性的能力。
Advances in breast imaging and other screening tests have prompted studies to evaluate and compare the consistency between experts’ ratings of existing with new screening tests. In clinical settings, medical experts make subjective assessments of screening test results such as mammograms. Consistency between experts’ ratings is evaluated by measures of inter-rater agreement or association. However, conventional measures, such as Cohen’s and Fleiss’ kappas, are unable to be applied or may perform poorly when studies consist of many experts, unbalanced data, or dependencies between experts’ ratings exist. Here we assess the performance of existing approaches including recently developed summary measures for assessing the agreement between experts’ binary and ordinal ratings when patients undergo two screening procedures. Methods to assess consistency between repeated measurements by the same experts are also described. We present applications to three large-scale clinical screening studies. Properties of these agreement measures are illustrated via simulation studies. Generally, a model-based approach provides several advantages over alternative methods including the ability to flexibly incorporate various measurement scales (i.e. binary or ordinal), large numbers of experts and patients, sparse data, and robustness to prevalence of underlying disease.
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影响因子：
2.3
作者：
Nelson KP;Edwards D
通讯作者：
Edwards D
影响因子：
2.7
作者：
FLEISS, JL;COHEN, J
通讯作者：
COHEN, J
影响因子：
158.5
作者：
Pisano, ED;Gatsonis, C;Rebner, M
通讯作者：
Rebner, M
影响因子：
19.7
作者：
Hendrick, R. Edward;Cole, Elodia B.;Gatsonis, Constantine
通讯作者：
Gatsonis, Constantine
影响因子：
2.7
作者：
BRENNAN, RL;PREDIGER, DJ
通讯作者：
PREDIGER, DJ