№ lp_2_1_06373
Supplementary methodological appendix detailing statistical imputation approaches for handling missing values in untargeted MS-based metabolomics data within the KORA study.
Title: Characterization of missingness in untargeted MS-based metabolomics data sets and evaluation of missing data handling strategies
Authors: Kieu Trinh Do; Simone Wahl; Johannes Raffler; Sophie Molnos; Michael Laimighofer; Jerzy Adamski; Karsten Suhre; Konstantin Strauch; Annette Peters; Christian Gieger; Claudia Langenberg; Isobel D. Stewart; Fabian J. Theis; Harald Grallert; Gabi Kastenmüller; Jan Krumsiek
Type of document: Supplementary information file
Subject: Missing data handling strategies in untargeted MS-based metabolomics
Methods described: Richardson & Ciampi (RC); Imputation by truncated sampling (ITS); Multiple imputation by truncated sampling (MITS); Imputation by chained equations (ICE-norm, ICE-pmm, ICE-adjR); K-nearest neighbor imputation (KNN-var, KNN-obs, KNN-obs-sel)
Software: R
R packages: tmvtnorm (version 1.4-9); mice (version 2.25); miceadds (version 1.5-0)
Data set: KORA
Covariates: Age; Sex; BMI
Statistical framework: Maximum likelihood estimation; Bayesian linear regression; Gibbs sampling; Multiple imputation according to Rubin
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