论文标题
在抑郁症研究中，功能回归中的多重插入功能回归中
Multiple imputation in functional regression with applications to EEG data in a depression study
论文作者
论文摘要
当前的源密度（CSD）功率不对称，一种源自脑电图（EEG）的度量，是主要抑郁症（MDD）的潜在生物标志物。尽管该度量在本质上是功能性的（定义在频域上），但通常在分析之前将其简化为标量值，可能会掩盖脑功能与MDD之间的关系。为了克服这个问题，我们试图使用功能性回归模型来估计CSD功率不对称和MDD诊断状态之间的关联，使用大型临床研究的数据调整年龄，性别，性别，认知能力和惯用性。不幸的是，将近40％的观察结果缺少其功能性脑电图数据，其认知能力得分或两者兼而有之。为了利用所有可用数据，我们建议通过处理标量和功能数据的链式方程向多个插补。我们还提出了鲁宾规则的扩展，以从乘以估算的数据集汇总估计值，以进行有效的推断。我们在模拟研究中研究了拟议扩展的性能，并将其应用于我们的临床研究数据。我们的分析表明，CSD功率不对称和诊断状态之间的关联取决于年龄和性别。
Current source density (CSD) power asymmetry, a measure derived from electroencephalography (EEG), is a potential biomarker for major depressive disorder (MDD). Though this measure is functional in nature (defined on the frequency domain), it is typically reduced to a scalar value prior to analysis, possibly obscuring the relationship between brain function and MDD. To overcome this issue, we sought to fit a functional regression model to estimate the association between CSD power asymmetry and MDD diagnostic status, adjusting for age, sex, cognitive ability, and handedness using data from a large clinical study. Unfortunately, nearly 40\% of the observations were missing either their functional EEG data, their cognitive ability score, or both. In order to take advantage of all of the available data, we propose an extension to multiple imputation by chained equations that handles both scalar and functional data. We also propose an extension to Rubin's Rules for pooling estimates from the multiply imputed data sets in order to conduct valid inference. We investigate the performance of the proposed extensions in a simulation study and apply them to our clinical study data. Our analysis reveals that the association between CSD power asymmetry and diagnostic status depends on both age and sex.
