A Sorting Statistic with Application in Neurological Magnetic Resonance Imaging of Autism. (29th March 2018)

• Record Type: Journal Article
• Authors: Levman, Jacob; Takahashi, Emi; Forgeron, Cynthia; MacDonald, Patrick; Stewart, Natalie; Lim, Ashley; Martel, Anne
• Other Names: Lin Feng-Huei Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2018(2018)
• Publication Date: 2018-03-29
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2018/8039075
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : 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.