title: Propensity score method for partially matched omics studies.
abstract: This paper focuses on the problem of partially matched samples in the presence of confounders. We propose using propensity score matching to adjust for confounding factors for the subset of data with incomplete pairs, followed by integrating the P-values computed from the complete and incomplete paired samples, respectively. Several simulations and a case study on DNA methylation are considered to evaluate the operating characteristics of the proposed method.
authors.affiliation: Departments of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
authors.forename: Pei-Fen
authors.initials: PF
authors.lastname: Kuan
keywords: confounders; full matching; microarray; observational studies; regression