The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data.
The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data.
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DetectDeviatingCells 算法是对观察数据中细胞错误检测工具包的有用补充。
DOI：
10.1016/j.jclinepi.2023.02.015
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发表时间：
2023
影响因子：
7.2
通讯作者：
Viviani L
中科院分区：
文献类型：
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作者：
Viviani L
目的评价DetectDeviatingCells（DDC）算法的错误检测性能，该算法在连续变量的观察（casewise）和变量（cellwise）水平上标记数据异常。我们在模拟dataset.Study设计和SettingWe模拟身高和体重数据的假设个人年龄2-20岁的其他方法的性能。我们根据预定的错误模式改变了高度值的比例。我们采用了DDC算法和其他错误检测方法（描述性统计，图，固定阈值规则，经典和鲁棒马氏距离），我们比较了错误检测性能与灵敏度，特异性，似然比，预测值，受试者工作特征（ROC）结果在我们选择的阈值下，所有方法在所有情况下的错误检测特异性都很好，并且对于多变量和鲁棒方法。DDC算法的性能与其他稳健的多变量方法相似。ROC曲线的分析表明，所有方法在粗差方面的性能相当（例如，错误的测量单元），但是DDC算法对于更复杂的错误模式优于其他算法（例如，转录错误，仍然是合理的，虽然极端）。ConclusionsThe DDC算法有可能提高错误检测过程中的观测数据。
ObjectivesWe evaluated the error detection performance of the DetectDeviatingCells (DDC) algorithm which flags data anomalies at observation (casewise) and variable (cellwise) level in continuous variables. We compared its performance to other approaches in a simulated dataset.Study Design and SettingWe simulated height and weight data for hypothetical individuals aged 2–20 years. We changed a proportion of height values according to predetermined error patterns. We applied the DDC algorithm and other error-detection approaches (descriptive statistics, plots, fixed-threshold rules, classic, and robust Mahalanobis distance) and we compared error detection performance with sensitivity, specificity, likelihood ratios, predictive values, and receiver operating characteristic (ROC) curves.ResultsAt our chosen thresholds error detection specificity was excellent across all scenarios for all methods and sensitivity was higher for multivariable and robust methods. The DDC algorithm performance was similar to other robust multivariable methods. Analysis of ROC curves suggested that all methods had comparable performance for gross errors (e.g., wrong measurement unit), but the DDC algorithm outperformed the others for more complex error patterns (e.g., transcription errors that are still plausible, although extreme).ConclusionsThe DDC algorithm has the potential to improve error detection processes for observational data.