Inverse probability weighting is an effective method to address selection bias during the analysis of high dimensional data.

ABSTRACT: Omics studies frequently use samples collected during cohort studies. Conditioning on sample availability can cause selection bias if sample availability is nonrandom. Inverse probability weighting (IPW) is purported to reduce this bias. We evaluated IPW in an epigenome-wide analysis testing the association between DNA methylation (261,435 probes) and age in healthy adolescent subjects (n = 114). We simulated age and sex to be correlated with sample selection and then evaluated four conditions: complete population/no selection bias (all subjects), naïve selection bias (no adjustment), and IPW selection bias (selection bias with IPW adjustment). Assuming the complete population condition represented the "truth," we compared each condition to the complete population condition. Bias or differ

SUBMITTER: Carry PM 

PROVIDER: S-EPMC8376760 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature