Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, United States.
Department of Biostatistics, Columbia University, New York , NY 10032, United States.
Biometrics. 2024 Jul 1;80(3). doi: 10.1093/biomtc/ujae064.
The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.
在许多科学研究中，需要从高维数据源（如神经影像学数据和遗传数据）中选择中介。在这项工作中，我们提出了一种从高维候选集中选择中介的多假设检验框架，并提出了一种方法，该方法扩展了最近在错误发现率（FDR）控制变量选择中引入的 knockoff 方法，以实现 FDR 控制的中介选择。我们证明了所提出的方法和算法在有限样本中实现了 FDR 控制。我们呈现了广泛的模拟结果，以证明与现有方法相比的功效和有限样本性能。最后，我们通过分析青少年大脑认知发展（ABCD）研究来说明该方法，该方法选择了几个静息态功能磁共振成像连通性标记作为中介，用于研究不良童年经历与 NIH 工具包中结晶复合评分之间的关系。
