B1854
Title: Approaches for extending multiple imputation to handle scalar and functional data
Authors: Adam Ciarleglio - George Washington University (United States) [presenting]
Abstract: Missing data are a common problem in biomedical research. Valid approaches for addressing this problem have been proposed and are regularly implemented in applications where the data are exclusively scalar-valued. However, with advances in technology and data storage, biomedical studies are beginning to collect both scalar and functional data, both of which may be subject to missingness. We propose extensions of multiple imputation with predictive mean matching and imputation by local residual draws as two approaches for handling missing scalar and functional data. The two methods are compared via a simulation study and applied to data from a study of subjects with major depressive disorder for which both clinical (scalar) and imaging (functional) data are available.
