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

Donneau, Anne-Françoise; Mauer, Murielle; Lambert, Philippe; Molenberghs, Geert; Albert, Adelin

doi:10.1080/10543406.2014.920864

Article (Scientific journals)

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

Donneau, Anne-Françoise; Mauer, Murielle; Lambert, Philippe et al.

2015 • In Journal of Biopharmaceutical Statistics, 25 (03), p. 570-601

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/159590

DOI
10.1080/10543406.2014.920864

PubMed
24905056

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Keywords :

ordinal variables; longitudinal analysis; non-monotone missingness; intermittent missingness; missing at random; multiple imputation

Abstract :

[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

Volume :

Issue :

Pages :

570-601

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 12 December 2013

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