title: Doubly robust multiple imputation using kernel-based techniques.
abstract: We consider the problem of estimating the marginal mean of an incompletely observed variable and develop a multiple imputation approach. Using fully observed predictors, we first establish two working models: one predicts the missing outcome variable, and the other predicts the probability of missingness. The predictive scores from the two models are used to measure the similarity between the incomplete and observed cases. Based on the predictive scores, we construct a set of kernel weights for the observed cases, with higher weights indicating more similarity. Missing data are imputed by sampling from the observed cases with probability proportional to their kernel weights. The proposed approach can produce reasonable estimates for the marginal mean and has a double robustness property, provided that one of the two working models is correctly specified. It also shows some robustness against misspecification of both models. We demonstrate these patterns in a simulation study. In a real-data example, we analyze the total helicopter response time from injury in the Arizona emergency medical service data.
authors.affiliation: Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin A232 Campus, PO Box 245211, Tucson, AZ, 85724, USA.|Arizona Cancer Center, College of Medicine, University of Arizona, Tucson, AZ, 85724, USA.
authors.forename: Chiu-Hsieh
authors.initials: CH
authors.lastname: Hsu
authors.affiliation: National Center for Health Statistics, CDC, Hyattsville, MD, 20782, USA.
authors.forename: Yulei
authors.initials: Y
authors.lastname: He
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
authors.forename: Yisheng
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Biostatistics and Bioinformatics, School of Public Health, Emory University, Atlanta, GA, 48109, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Long
authors.affiliation: Department of Surgery, College of Medicine, University of Arizona, Tucson, AZ, 85724, USA.
authors.forename: Randall
authors.initials: R
authors.lastname: Friese
mesh_terms: D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011336:Probability
keywords: Bandwidth; Bootstrap; Local imputation; Model misspecification; Nonparametric