A measure of association for ordered categorical data in population-based studies.

A measure of association for ordered categorical data in population-based studies.
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DOI:
10.1177/0962280216643347
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发表时间:
2018-03
影响因子:
2.3
通讯作者:
Edwards D
Edwards D
中科院分区:
医学3区
文献类型:
--
作者:
Nelson KP;Edwards D

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有序分类量表通常用于在筛查和诊断测试(如乳房X线摄影)中定义患者的疾病状态。当使用有序分类量表评估许多评定者对患者疾病或健康状况的分类之间的关联时,一致性研究中出现了挑战。在本文中,我们描述了一种基于人口的方法和机会校正的关联测度来评估多个评分者的顺序分类之间的关系强度,其中可以容纳任何数量的评分者。与Shrout和Fleiss的组内相关系数相比,所提出的关联性度量对于疾病患病率的变化是不变的。我们展示了如何使用随机效应来探索个别评分员的独特特征。仿真研究表明,所提出的方法在不同的假设下的性能。该方法适用于两个大规模的协议研究乳腺癌筛查和前列腺癌的严重程度。
Ordinal classification scales are commonly used to define a patient’s disease status in screening and diagnostic tests such as mammography. Challenges arise in agreement studies when evaluating the association between many raters’ classifications of patients’ disease or health status when an ordered categorical scale is used. In this paper, we describe a population-based approach and chance-corrected measure of association to evaluate the strength of relationship between multiple raters’ ordinal classifications where any number of raters can be accommodated. In contrast to Shrout and Fleiss’ intraclass correlation coefficient, the proposed measure of association is invariant with respect to changes in disease prevalence. We demonstrate how unique characteristics of individual raters can be explored using random effects. Simulation studies are conducted to demonstrate the properties of the proposed method under varying assumptions. The methods are applied to two large-scale agreement studies of breast cancer screening and prostate cancer severity.
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