title: Imputation for incomplete high-dimensional multivariate normal data using a common factor model.
abstract: It is common in applied research to have large numbers of variables measured on a modest number of cases. Even with low rates of missingness on individual variables, such data sets can have a large number of incomplete cases. Here we present a new method for handling missing continuously scaled items in multivariate data, based on extracting common factors to reduce the number of covariance parameters to be estimated in a multivariate normal model. The technique is compared in several simulation settings to available-case analysis and to a multivariate normal model with a ridge prior. The method is also illustrated on a study with over 100 variables evaluating an emergency room intervention for adolescents who attempted suicide.
authors.affiliation: Department of Biostatistics and Applied Mathematics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Box 447, Houston 77030, USA. jwsong@mdanderson.org
authors.forename: Juwon
authors.initials: J
authors.lastname: Song
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Belin
mesh_terms: D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D013406:Suicide, Attempted / Q000523:psychology; D014481:United States