Methods for Dealing With Missing Covariate Data in Epigenome-Wide Association Studies.

ABSTRACT: 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 even

SUBMITTER: Mills HL 

PROVIDER: S-EPMC6825836 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature