Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS, Canada B2G 2W5.
Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, No. 456, Boston, MA 02215, USA.
J Healthc Eng. 2018 Mar 29;2018:8039075. doi: 10.1155/2018/8039075. eCollection 2018.
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 statistic. Cohen's 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 statistic and is demonstrated to be capable of identifying feature measurements of potential interest for which Cohen's statistic implies the measurement would be of little use. This proposed sorting statistic has been evaluated on a large clinical autism dataset from , , demonstrating that it can potentially play a constructive role in future healthcare technologies.
效应量是指在单个测量中评估两组样本之间差异程度的指标。在医学研究中，通常使用科恩的统计量来评估效应量。科恩的统计量假设平均值是数字分布位置的良好估计值，并且还假设基础数据分布是高斯（或钟形）分布。在本文中，我们提出了一种替代的评估统计量，可以以类似于传统效应量计算的方式量化两个数据分布之间的差异；然而，所提出的方法避免了对基础数据分布形状的假设。所提出的排序统计量与科恩的统计量进行了比较，并证明它能够识别出潜在感兴趣的特征测量值，而科恩的统计量则表明这些测量值用处不大。该排序统计量已在来自 的大型临床自闭症数据集上进行了评估，证明它可能在未来的医疗保健技术中发挥建设性作用。
