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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18577

Title: A simulation study comparing multiple imputation methods for incomplete longitudinal ordinal data
Authors: Donneau, A.F.
Mauer, M.
Molenberghs, Geert
Albert, A.
Issue Date: 2015
Citation: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 44 (5), p. 1311-1338
Abstract: Multiple imputation (MI) is now a reference solution for handling missing data. The default method for MI is the Multivariate Normal Imputation (MNI) algorithm which is based on the multivariate normal distribution. In the presence of longitudinal ordinal missing data, where the Gaussian assumption is no longer valid, application of the MNI method is questionable. This simulation study compares the performance of the MNI and ordinal imputation regression model for incomplete longitudinal ordinal data for situations covering various numbers of categories of the ordinal outcome, time occasions, sample sizes, rates of missingness, well-balanced and skewed data.
Notes: E-mail Addresses:afdonneau@ulg.ac.be
URI: http://hdl.handle.net/1942/18577
DOI: 10.1080/03610918.2013.818690
ISI #: 000343647300016
ISSN: 0361-0918
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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