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Abstract：
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.
摘要：
需要从高维数据源中选择中介，比如神经影像数据和基因数据，出现在许多科学研究中。在这项工作中,我们制定了一个多假设检验框架，用于从高维候选集中选择介体，并提出了一种方法，这扩展了最近在错误发现率（FDR）控制变量选择中的最新发展，该变量选择具有FDR控制的介体。我们证明了所提出的方法和算法实现了有限样本FDR控制。我们提供了大量的仿真结果，以证明与现有方法相比的功率和有限样本性能。最后,我们展示了分析青少年脑认知发育（ABCD）研究的方法，其中所提出的方法选择了几种静息态功能磁共振成像连接标志物作为NIH工具箱中不良儿童事件与结晶复合评分之间关系的介质.
