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The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data by Laura Viviani; Ian R. White; Elizabeth J. Williamson; James Carpenter; Jan van der Meulen; David A. Cromwell is a Decision Sciences article available to read on EtoBox.
What is The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data about?
## Objectives 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. ## Study Design and Setting 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. ## Results 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
Who reads The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data?
It is typically read by researchers, students, and practitioners in Decision Sciences.
- Author
- Laura Viviani; Ian R. White; Elizabeth J. Williamson; James Carpenter; Jan van der Meulen; David A. Cromwell
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Decision Sciences (Social Sciences)