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Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings

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Donneau, Anne-Françoise [ULG] Mauer, Murielle [EORTC, Belgium] Lambert, Philippe [UCL] Molenberghs, Geert [Univrsity of Hasselt, Belgium] Albert, Adelin [ULG]

The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI method can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for the longitudinal ordinal data setting under the missing at random (MAR) assumption is not well documented. This paper intends to fill this gap by conducting a large simulation study on the estimation of the parameters of a longitudinal proportional odds model. The two MI methods are also illustrated in quality of life data from a cancer clinical trial.

Document type Article de périodique (Journal article) – Article de recherche
Publication date 2015
Language Anglais
Journal information "Journal of Biopharmaceutical Statistics" - Vol. 25, no. 3, p. 570-601 (2015)
Peer reviewed yes
Publisher Taylor & Francis Inc. ((United States) Philadelphia)
issn 1054-3406
e-issn 1520-5711
Publication status Publié
Affiliation UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Keywords ordinal variables ; longitudinal analysis ; non-monotone missingness ; intermittent missingness ; missing at random ; multiple imputation
Links
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Bibliographic reference Donneau, Anne-Françoise ; Mauer, Murielle ; Lambert, Philippe ; Molenberghs, Geert ; Albert, Adelin. Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings. In: Journal of Biopharmaceutical Statistics, Vol. 25, no. 3, p. 570-601 (2015)
Permanent URL http://hdl.handle.net/2078.1/142130