Receiver operating characteristic plot and area under the curve with binary classifiers: pragmatic analysis of cognitive screening instruments.

IF 2.3 Q3 CLINICAL NEUROLOGY Neurodegenerative disease management Pub Date : 2021-10-01 Epub Date: 2021-09-27 DOI:10.2217/nmt-2021-0013
Gashirai K Mbizvo, Andrew J Larner
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引用次数: 4

Abstract

Aim: To examine whether receiver operating characteristic plots and area under the curve (AUC) values may be potentially misleading when assessing cognitive screening instruments as binary predictors rather than as categorical or continuous scales. Materials & methods: AUC was calculated using different methods (rank-sum, diagnostic odds ratio) using data from test accuracy studies of two binary classifiers of cognitive status (applause sign, attended with sign), a screener producing categorical data (Codex), and a continuous scale screening test (Mini-Addenbrooke's Cognitive Examination). Results: For all screeners, AUC calculated using diagnostic odds ratio method was greater than using rank-sum method. When Codex and Mini-Addenbrooke's Cognitive Examination were analyzed as binary (single fixed threshold) tests, AUC using rank-sum method was lower than when screeners were analyzed as categorical or continuous scales, respectively. Conclusion: If cognitive screeners producing categorical or continuous measures are dichotomized, calculated AUC may be an underestimate, thus affecting screening test accuracy.

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二元分类器的受试者工作特征图和曲线下面积:认知筛选工具的语用分析。
目的:研究受试者工作特征图和曲线下面积(AUC)值是否可能在评估认知筛查工具作为二元预测因子而不是分类或连续量表时产生误导。材料和方法:AUC使用不同的方法(秩和、诊断优势比)计算,使用来自两个认知状态二元分类器(鼓掌标志、参加标志)、产生分类数据的筛选器(Codex)和连续量表筛选测试(Mini-Addenbrooke's cognitive Examination)的测试准确性研究数据。结果:对于所有筛查者,诊断优势比法计算的AUC均大于秩和法。当Codex和Mini-Addenbrooke认知检查作为二元(单一固定阈值)测试进行分析时,使用秩和方法的AUC分别低于筛选者作为分类或连续量表进行分析时的AUC。结论:如果对产生分类或连续测量的认知筛选者进行二分类,计算出的AUC可能会被低估,从而影响筛选测试的准确性。
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CiteScore
4.30
自引率
0.00%
发文量
35
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