CopyMean: A new method to predict monotone missing values in longitudinal studies.
BACKGROUND
Longitudinal studies are those in which the same variable is repeatedly measured at different times. More likely than others, these studies suffer from missing values. Because the missing values may impact the statistical analyses, it is important that they be dealt with properly.


METHODS
In this paper, we present "CopyMean", a new method to impute (predict) monotone missing values. We compared its efficiency to sixteen imputation methods dedicated to the treatment of missing values in longitudinal data. All these methods were tested on four datasets, real or artificial, presenting markedly different caracteristics.


RESULTS
The analysis showed that CopyMean was more efficient in almost all situations.
Inserm UMR U1027, Research Unit on Perinatal Epidemiology and Childhood Disabilities, Adolescent Health, Université Paul Sabatier, Toulouse III, Toulouse, France; CeRSM (EA 2931), UFR STAPS, Université de Paris Ouest-Nanterre-La défense, 92000 Nanterre, France. Electronic address: christophe.genolini@u-paris10.fr.
Christophe
C
Genolini
Inserm UMR U1027, Research Unit on Perinatal Epidemiology and Childhood Disabilities, Adolescent Health, Université Paul Sabatier, Toulouse III, Toulouse, France.
Amandine
A
Lacombe
Hospices Civils de Lyon, Service de Biostatistique, F-69003 Lyon, France; CNRS, UMR5558, Laboratoire de Biomtrie et Biologie Evolutive, Equipe Biostatistique-Sant, F-69100 Villeurbanne, France.
René
R
Écochard
Hospices Civils de Lyon, Service de Biostatistique, F-69003 Lyon, France; CNRS, UMR5558, Laboratoire de Biomtrie et Biologie Evolutive, Equipe Biostatistique-Sant, F-69100 Villeurbanne, France.
Fabien
F
Subtil
D000428:Alcohol Drinking; D015415:Biomarkers; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008597:Menstrual Cycle
D015415:Biomarkers
Imputation; Longitudinal data; Missing data