High-dimensional causal mediation analysis based on partial linear structural equation models
High-dimensional causal mediation analysis based on partial linear structural equation models
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DOI：
10.1016/j.csda.2022.107501
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Xizhen Cai;Yeying Zhu;Yuan Huang;Debashis Ghosh
中科院分区：
文献类型：
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作者：
Xizhen Cai;Yeying Zhu;Yuan Huang;Debashis Ghosh
近年来，因果中介分析逐渐流行。中介分析的目的是了解暴露对结果的直接影响以及对从暴露到结果的途径的中介影响。当介质数为高维数时，提出了一组广义结构方程来估计中介分析的直接和间接效应。具体而言，一个两步的程序被认为是惩罚框架可以通过执行变量选择。使用部分线性模型解释各模型中治疗前混杂因素与响应变量之间的非线性关系。开发的程序估计系数的治疗和介质的结构模型。所得到的估计量可以解释为因果效应，而不对模型结构施加线性假设。Sobel方法在获得估计的联合间接效应的标准误差和置信区间方面的性能也在模拟研究中进行了评估。仿真结果表明，该方法具有上级性能。它适用于流行病学研究，其目标是了解DNA甲基化如何介导儿童创伤对人类应激反应调节的影响。
Causal mediation analysis has become popular in recent years. The goal of mediation analyses is to learn the direct effects of exposure on outcome as well as mediated effects on the pathway from exposure to outcome. A set of generalized structural equations to estimate the direct and indirect effects for mediation analysis is proposed when the number of mediators is of high-dimensionality. Specifically, a two-step procedure is considered where the penalization framework can be adopted to perform variable selection. A partial linear model is used to account for a nonlinear relationship among pre-treatment confounders and the response variable in each model. Procedures for estimating the coefficients for the treatment and the mediators in the structural models are developed. The obtained estimators can be interpreted as causal effects without imposing a linear assumption on the model structure. The performance of Sobel's method in obtaining the standard error and confidence interval for the estimated joint indirect effect is also evaluated in simulation studies. Simulation results show a superior performance of the proposed method. It is applied to an epidemiologic study in which the goal is to understand how DNA methylation mediates the effect of childhood trauma on regulation of human stress reactivity.