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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17789

Title: Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings
Authors: Donneau, A.F.
Mauer, M.
Lambert, Philippe
Molenberghs, Geert
Albert, A.
Issue Date: 2014
Citation: Journal of Biopharmaceutical Statistics, 25 (3), p. 570-601
Abstract: The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI methods can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for longitudinal ordinal data setting is not well documented. This paper intends to ll 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 on a real dataset of quality of life in a cancer clinical trial.
Notes: Donneau, AF (reprint author), Univ Liege, Dept Publ Hlth, Med Informat & Biostat, Sart Tilman B23, B-4000 Liege, Belgium. afdonneau@ulg.ac.be
URI: http://hdl.handle.net/1942/17789
DOI: 10.1080/10543406.2014.920864
ISI #: 000353386300013
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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