title: Covariate adjusted differential variability analysis of DNA methylation with propensity score method.
abstract: It has been proposed recently that differentially variable CpG methylation (DVC) may contribute to transcriptional aberrations in human diseases. In large scale epigenetic studies, potential confounders could affect the observed methylation variabilities and need to be accounted for. In this paper, we develop a robust statistical model for differential variability DVC analysis that accounts for potential confounding covariates by utilizing the propensity score method. Our method is based on a weighted score test on strata generated propensity score stratification. To the best of our knowledge, this is the first proposed statistical method for detecting DVCs that adjusts for confounding covariates. We show that this method is robust against model misspecification and achieves good operating characteristics based on extensive simulations and a case study.
authors.forename: Pei Fen
authors.initials: PF
authors.lastname: Kuan
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D018899:CpG Islands; D019175:DNA Methylation; D005260:Female; D006801:Humans; D015233:Models, Statistical; D057216:Propensity Score