Xu Yixi, Zhao Yi
Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 West 10th Street, Indianapolis, Indiana, 46202, United States.
Biostatistics. 2024 Dec 31;26(1). doi: 10.1093/biostatistics/kxaf004.
This study introduces a mediation analysis framework when the mediator is a graph. A Gaussian covariance graph model is assumed for graph presentation. Causal estimands and assumptions are discussed under this presentation. With a covariance matrix as the mediator, a low-rank representation is introduced and parametric mediation models are considered under the structural equation modeling framework. Assuming Gaussian random errors, likelihood-based estimators are introduced to simultaneously identify the low-rank representation and causal parameters. An efficient computational algorithm is proposed and asymptotic properties of the estimators are investigated. Via simulation studies, the performance of the proposed approach is evaluated. Applying to a resting-state fMRI study, a brain network is identified within which functional connectivity mediates the sex difference in the performance of a motor task.
本研究引入了一种当中介变量为图时的中介分析框架。假设用高斯协方差图模型来表示图。在此表示下讨论了因果估计量和假设。以协方差矩阵作为中介变量，引入了低秩表示，并在结构方程建模框架下考虑了参数中介模型。假设高斯随机误差，引入基于似然的估计量以同时识别低秩表示和因果参数。提出了一种有效的计算算法，并研究了估计量的渐近性质。通过模拟研究，评估了所提方法的性能。将其应用于静息态功能磁共振成像研究，识别出一个脑网络，其中功能连接介导了运动任务表现中的性别差异。
