A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data |
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| Authors: | A F Donneau M Mauer G Molenberghs A Albert |
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| Institution: | 1. Medical Informatics and Biostatistics, University of Liège, Liège, Belgium;2. EORTC Headquarters, Departments of Statistics and Quality of Life, Brussels, Belgium;3. I-BioStat, University of Hasselt, Diepenbeek, Belgium - I-BioStat, Katholieke University of Leuven, Leuven, Belgium |
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| 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 that 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 In the presence of longitudinal ordinal missing data, where the Gaussian assumption is no longer valid, application of the MNI method is questionable.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. |
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| Keywords: | Longitudinal analysis Missing at random Multiple imputation Ordinal variables |
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