Correction to: Scientific Reports 10.1038/srep34468, published online 05 October 2016
This Article contains errors.
We were alerted to a potential error in one of our analyses. We then reviewed the analysis and found a mistake in the Receiver Operating Characteristic (ROC) curve computation for one of the finger tapping test (the alternating finger tapping test). This error was due to automatic conversion of the alternating finger tapping missing data to a value of 0 by the functions implemented in the “Scikit-learn” Python library1 used at the time of writing the paper (v. 0.13.1). The most recent versions fixed this problem.
We updated our “Scikit-learn” Python library and re-ran our statistical analysis. We then confirmed our results using the pROC package implemented in R2. We find that the Area under the ROC curve (AUC) for the alternative finger tapping test is 0.83, and not 0.75 reported in the published Article.
As a result, in the Abstract,
“The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).”
should read:
“The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.83) and single key tapping (AUC = 0.61).”
In the legend of Figure 3,
“The nQi score shows the best performance in comparison with alternating finger tapping (p < 0.001) and single key tapping (p < 0.001).”
should read:
“The nQi score shows comparable performance in to alternating finger tapping (p = 0.699) and superior to single key tapping (p = 0.01).”
and
“In our cohort, the former showed better performance than the latter (p = 0.008).”
should read:
“In our cohort, the former showed better performance than the latter (p = 0.004).”
The correct Figure 3 and accompanying legend, with correctly computed AUCs, appears below as Figure 1.
These changes do not alter the conclusions of the Article.
References
- 1.Pedregosa F, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–2830. [Google Scholar]
- 2.Robin, X. et al. pROC: Display and Analyze ROC Curves. CRANhttps://cran.r-project.org/web/packages/pROC/ (2018).