目的:我们评估了DetectDeviatingCells(DDC)算法的错误检测性能,它在连续变量中的观察(逐例)和变量(单元)级别标记数据异常。我们将其性能与模拟数据集中的其他方法进行了比较。
方法:我们模拟了2-20岁假设个体的身高和体重数据。我们根据预定的错误模式改变了高度值的比例。我们应用了DDC算法和其他错误检测方法(描述性统计,地块,固定阈值规则,经典和鲁棒的马氏距离),我们比较了误差检测性能和灵敏度,特异性,似然比,预测值和ROC曲线。
结果:在我们选择的阈值下,所有方法的错误检测特异性在所有情况下都非常好,多变量和稳健方法的灵敏度更高.DDC算法的性能与其他稳健的多变量方法相似。对ROC曲线的分析表明,所有方法在粗差(例如错误的测量单位)方面都具有可比的性能,但是DDC算法在更复杂的错误模式上优于其他算法(例如仍然合理的转录错误,虽然极端)。
结论:DDC算法有可能改善观测数据的错误检测过程。
We 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.
We 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.
At 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).
The DDC algorithm has the potential to improve error detection processes for observational data.