[en] 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.
Disciplines :
Mathematics
Author, co-author :
Donneau, Anne-Françoise ; Université de Liège - ULiège > Département des sciences de la santé publique > Informatique médicale et biostatistique
Mauer, Murielle
Lambert, Philippe ; Université de Liège - ULiège > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales
Molenberghs, Geert
Albert, Adelin ; Université de Liège - ULiège > Département des sciences de la santé publique > Département des sciences de la santé publique
Language :
English
Title :
Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings
Publication date :
2015
Journal title :
Journal of Biopharmaceutical Statistics
ISSN :
1054-3406
eISSN :
1520-5711
Publisher :
Taylor & Francis, Philadelphia, United States - Pennsylvania
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