Koerner Tess K, Zhang Yang
Department of Speech-Language-Hearing Sciences, University of Minnesota, Minneapolis, MN 55455, USA.
Center for Neurobehavioral Development, University of Minnesota, Minneapolis, MN 55455, USA.
Brain Sci. 2017 Feb 27;7(3):26. doi: 10.3390/brainsci7030026.
Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.
神经生理学研究通常旨在检验同一组参与者在不同测试条件、时间点或分析技术下所测指标之间的关系。能够考虑重复测量和多变量预测变量的适当统计技术是成功进行数据分析和解释不可或缺的关键要素。这项工作使用两项最近发表的听觉电生理学研究的数据，实现并比较了传统的皮尔逊相关性分析和线性混合效应（LME）回归模型。对于这两项研究中的特定研究问题，皮尔逊相关性检验不适用于确定噪声中言语识别的行为反应与多种神经生理学指标之间的强度关系，因为跨聆听条件的神经反应被简单地视为独立测量值。相比之下，LME模型提供了一种系统的方法，可纳入固定效应和随机效应项，以处理聆听条件的分类分组因素、多种测量中受试者间的基线差异以及预测变量之间的相关结构。总体而言，对比数据展示了应用混合效应模型来恰当考虑多个预测变量之间内在关系的优势和必要性，这对于从神经关联和生物标志物角度对人类行为进行恰当的统计建模和解释具有重要意义。
