Methods for Dealing With Missing Covariate Data in Epigenome-Wide Association Studies.
Methods for Dealing With Missing Covariate Data in Epigenome-Wide Association Studies.
复制标题
表观基因组范围关联研究中缺失协变量数据的处理方法。
DOI：
10.1093/aje/kwz186
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发表时间：
2019
影响因子：
5
通讯作者：
Mills HL
中科院分区：
文献类型：
--
作者：
Mills HL
多重插补（MI）是处理缺失数据的成熟方法。当用高维结局数据插补缺失协变量时，MI是计算密集型的（例如，表观全基因组关联研究（EWAS）中的DNA甲基化数据），因为每个结果变量都必须包括在插补模型中，以避免使关联偏向零。相反，EWAS分析仅限于完整病例，限制了统计功效并可能导致偏倚。我们使用模拟来比较5 MI方法的高维数据下2缺失机制。所有插补方法的把握度均高于完整病例（C-C）分析。为每个变量分别插补缺失值在计算上是低效的，但是将位点随机划分为均匀大小的箱提高了效率并且给出了低偏差。仅使用C-C分析确定的研究中心子集进行插补的方法存在零偏倚。然而，如果将这些子集添加到站点的随机箱中，则该偏倚减小。最佳方法被应用到一个EWAS与缺失的协变量。所有方法都在C-C分析中确定了额外的位点，其中许多位点已在其他研究中重复。这些方法也适用于其他高维数据集，包括快速扩展的“组学”研究领域。
Multiple imputation (MI) is a well-established method for dealing with missing data. MI is computationally intensive when imputing missing covariates with high-dimensional outcome data (e.g., DNA methylation data in epigenome-wide association studies (EWAS)), because every outcome variable must be included in the imputation model to avoid biasing associations towards the null. Instead, EWAS analyses are reduced to only complete cases, limiting statistical power and potentially causing bias. We used simulations to compare 5 MI methods for high-dimensional data under 2 missingness mechanisms. All imputation methods had increased power over complete-case (C-C) analyses. Imputing missing values separately for each variable was computationally inefficient, but dividing sites at random into evenly sized bins improved efficiency and gave low bias. Methods imputing solely using subsets of sites identified by the C-C analysis suffered from bias towards the null. However, if these subsets were added into random bins of sites, this bias was reduced. The optimal methods were applied to an EWAS with missingness in covariates. All methods identified additional sites over the C-C analysis, and many of these sites had been replicated in other studies. These methods are also applicable to other high-dimensional data sets, including the rapidly expanding area of “-omics” studies.