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Inverse probability weighting is an effective method to address selection bias during the analysis of high dimensional data
Author(s) -
Carry Patrick M.,
Vanderlinden Lauren A.,
Dong Fran,
Buckner Teresa,
Litkowski Elizabeth,
Vigers Timothy,
Norris Jill M.,
Kechris Katerina
Publication year - 2021
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22418
Subject(s) - weighting , selection (genetic algorithm) , statistics , inverse probability weighting , inverse , computer science , mathematics , econometrics , artificial intelligence , medicine , propensity score matching , geometry , radiology
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 difference in associations between age and methylation was reduced in the IPW condition versus the naïve condition. However, genomic inflation and type 1 error were higher in the IPW condition relative to the naïve condition. Postadjustment using bacon, type 1 error and inflation were similar across all conditions. Power was higher under the IPW condition compared with the naïve condition before and after inflation adjustment. IPW methods can reduce bias in genome‐wide analyses. Genomic inflation is a potential concern that can be minimized using methods that adjust for inflation.