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Journal of Healthcare Engineering
Volume 2018, Article ID 8039075, 6 pages
Research Article

A Sorting Statistic with Application in Neurological Magnetic Resonance Imaging of Autism

1Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS, Canada B2G 2W5
2Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 1 Autumn Street, No. 456, Boston, MA 02215, USA
3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
4Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

Correspondence should be addressed to Jacob Levman; ac.xfts@namvelj

Received 28 November 2017; Accepted 29 January 2018; Published 29 March 2018

Academic Editor: Feng-Huei Lin

Copyright © 2018 Jacob Levman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Effect size refers to the assessment of the extent of differences between two groups of samples on a single measurement. Assessing effect size in medical research is typically accomplished with Cohen’s d statistic. Cohen’s d statistic assumes that average values are good estimators of the position of a distribution of numbers and also assumes Gaussian (or bell-shaped) underlying data distributions. In this paper, we present an alternative evaluative statistic that can quantify differences between two data distributions in a manner that is similar to traditional effect size calculations; however, the proposed approach avoids making assumptions regarding the shape of the underlying data distribution. The proposed sorting statistic is compared with Cohen’s d statistic and is demonstrated to be capable of identifying feature measurements of potential interest for which Cohen’s d statistic implies the measurement would be of little use. This proposed sorting statistic has been evaluated on a large clinical autism dataset from Boston Children’s Hospital, Harvard Medical School, demonstrating that it can potentially play a constructive role in future healthcare technologies.