PDF(Pubmed)
Abstract：
BACKGROUND: Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it\'s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation.
METHODS: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing.
RESULTS: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene.
CONCLUSIONS: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.
METHODS: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing.
RESULTS: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene.
CONCLUSIONS: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.
摘要：
背景：中介分析是确定影响健康结果的因果途径的因素的有力工具。中介分析已扩展到研究高维数据设置中的大量潜在中介。观察性研究中混杂因素的存在是不可避免的。因此，调整潜在的混杂因素是高维中介分析(HDMA)的重要组成部分。虽然倾向得分（PS）相关的方法，如倾向得分回归调整（PSR）和逆概率加权（IPW）已被提出来解决这个问题，基于PS的方法具有极端倾向得分分布的特征会导致有偏估计。
方法：在本文中，我们将重叠加权（OW）技术集成到HDMA工作流程中，并提出了一个简洁而强大的高维中介分析程序，包括OW混杂调整，确定独立性筛选(SIS)，去偏见的套索惩罚，以及混合零分布基础的联合显著性检验。我们将提出的方法与现有的基于PS的混杂调整方法进行了比较，SIS,极小极大凹惩罚(MCP)变量选择，和经典的联合显著性检验。
结果：仿真研究表明，所提出的程序在中介选择和估计方面具有最佳性能。拟议的程序产生了最高的真实阳性率，可接受的错误发现比例水平，和较低的均方误差。在基于GSE117859数据集的基因表达综合数据库的实证研究中,我们发现，吸烟史可能导致估计的自然杀伤（NK）细胞水平降低通过一些甲基化标记的调解作用，主要包括CNP基因中的甲基化位点cg13917614和LILRA2基因中的cg16893868。
结论：所提出的方法具有更高的功率，足够的错误发现率控制，和精确的中介效应估计。同时,在存在混杂因素的情况下实施是可行的。因此，我们的方法值得在HDMA研究中考虑。
方法：在本文中，我们将重叠加权（OW）技术集成到HDMA工作流程中，并提出了一个简洁而强大的高维中介分析程序，包括OW混杂调整，确定独立性筛选(SIS)，去偏见的套索惩罚，以及混合零分布基础的联合显著性检验。我们将提出的方法与现有的基于PS的混杂调整方法进行了比较，SIS,极小极大凹惩罚(MCP)变量选择，和经典的联合显著性检验。
结果：仿真研究表明，所提出的程序在中介选择和估计方面具有最佳性能。拟议的程序产生了最高的真实阳性率，可接受的错误发现比例水平，和较低的均方误差。在基于GSE117859数据集的基因表达综合数据库的实证研究中,我们发现，吸烟史可能导致估计的自然杀伤（NK）细胞水平降低通过一些甲基化标记的调解作用，主要包括CNP基因中的甲基化位点cg13917614和LILRA2基因中的cg16893868。
结论：所提出的方法具有更高的功率，足够的错误发现率控制，和精确的中介效应估计。同时,在存在混杂因素的情况下实施是可行的。因此，我们的方法值得在HDMA研究中考虑。
