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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 | Source: | 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 In the presence of longitudinal ordinal missing data, where the Gaussian assumption is no longer valid, application of the MNI method is questionable.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 | Keywords: | ordinal variables; longitudinal analysis; missing at random; multiple imputation | Document URI: | http://hdl.handle.net/1942/18577 | ISSN: | 0361-0918 | e-ISSN: | 1532-4141 | DOI: | 10.1080/03610918.2013.818690 | ISI #: | 000343647300016 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2015 |
Appears in Collections: | Research publications |
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