Missing value imputation in longitudinal measures of alcohol consumption

Détails

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Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_2CFB6A0749D5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Missing value imputation in longitudinal measures of alcohol consumption
Périodique
International Journal of Methods in Psychiatric Research
Auteur(s)
Grittner U., Gmel G., Ripatti S., Bloomfield K., Wicki M.
ISSN
1049-8931
Statut éditorial
Publié
Date de publication
2011
Peer-reviewed
Oui
Volume
20
Numéro
1
Pages
50-61
Langue
anglais
Résumé
Attrition in longitudinal studies can lead to biased results. The study is motivated by the unexpected observation that alcohol consumption decreased despite increased availability, which may be due to sample attrition of heavy drinkers. Several imputation methods have been proposed, but rarely compared in longitudinal studies of alcohol consumption. The imputation of consumption level measurements is computationally particularly challenging due to alcohol consumption being a semi-continuous variable (dichotomous drinking status and continuous volume among drinkers), and the non-normality of data in the continuous part. Data come from a longitudinal study in Denmark with four waves (2003-2006) and 1771 individuals at baseline. Five techniques for missing data are compared: Last value carried forward (LVCF) was used as a single, and Hotdeck, Heckman modelling, multivariate imputation by chained equations (MICE), and a Bayesian approach as multiple imputation methods. Predictive mean matching was used to account for non-normality, where instead of imputing regression estimates, "real" observed values from similar cases are imputed. Methods were also compared by means of a simulated dataset. The simulation showed that the Bayesian approach yielded the most unbiased estimates for imputation. The finding of no increase in consumption levels despite a higher availability remained unaltered. Copyright (C) 2011 John Wiley & Sons, Ltd.
Mots-clé
panel surveys, missing data, multiple imputation, Bayesian models, alcohol consumption, MULTIPLE IMPUTATION, HOT-DECK, SPECIFICATION, STABILITY, ATTRITION, SELECTION, DISCRETE, DRINKING, OUTCOMES, MODEL
Pubmed
Web of science
Création de la notice
18/04/2011 15:09
Dernière modification de la notice
20/08/2019 13:12
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