Department of Psychology, Seton Hall University South Orange, NJ, USA.
Front Hum Neurosci. 2013 May 20;7:211. doi: 10.3389/fnhum.2013.00211. eCollection 2013.
neglect arises from impairment in distinct brain networks leading to large between-subject variability in baseline symptoms and recovery trajectories. Studies enrolling medically ill, disabled patients, may suffer from missing, unbalanced data, and small sample sizes. Finally, assessment of rehabilitation requires a description of continuous recovery trajectories. Unfortunately, the statistical method currently employed in most studies of neglect treatment [repeated measures analysis of variance (ANOVA), rANOVA] does not well-address these issues. Here we review an alternative, mixed linear modeling (MLM), that is more appropriate for assessing change over time. MLM better accounts for between-subject heterogeneity in baseline neglect severity and in recovery trajectory. MLM does not require complete or balanced data, nor does it make strict assumptions regarding the data structure. Furthermore, because MLM better models between-subject heterogeneity it often results in increased power to observe treatment effects with smaller samples. After reviewing current practices in the field, and the assumptions of rANOVA, we provide an introduction to MLM. We review its assumptions, uses, advantages, and disadvantages. Using real and simulated data, we illustrate how MLM may improve the ability to detect effects of treatment over ANOVA, particularly with the small samples typical of neglect research. Furthermore, our simulation analyses result in recommendations for the design of future rehabilitation studies. Because between-subject heterogeneity is one important reason why studies of neglect treatments often yield conflicting results, employing statistical procedures that model this heterogeneity more accurately will increase the efficiency of our efforts to find treatments to improve the lives of individuals with neglect.
忽视是由大脑网络特定区域的损伤引起的，这导致了基线症状和恢复轨迹的个体间的巨大差异。招募患有医学疾病和残疾的患者的研究可能会受到缺失、不平衡数据和小样本量的影响。最后，康复评估需要描述连续的恢复轨迹。不幸的是，目前大多数忽视治疗研究中使用的统计方法[重复测量方差分析（ANOVA），rANOVA]并不能很好地解决这些问题。在这里，我们回顾了一种替代方法，即混合线性模型（MLM），它更适合评估随时间的变化。MLM 更好地解释了基线忽视严重程度和恢复轨迹的个体间异质性。MLM 不要求完整或平衡的数据，也不严格假设数据结构。此外，由于 MLM 更好地模拟了个体间的异质性，因此它通常可以用更小的样本量获得更大的观察治疗效果的能力。在回顾了该领域的当前实践和 rANOVA 的假设之后，我们对 MLM 进行了介绍。我们回顾了它的假设、用途、优点和缺点。使用真实和模拟数据，我们说明了 MLM 如何通过 ANOVA 提高检测治疗效果的能力，特别是在忽视研究中常见的小样本情况下。此外，我们的模拟分析结果为未来康复研究的设计提出了建议。由于个体间的异质性是忽视治疗研究结果经常相互矛盾的一个重要原因，因此采用更准确地模拟这种异质性的统计方法将提高我们寻找治疗方法以改善忽视患者生活的效率。
