CopyMean: A new method to predict monotone missing values in longitudinal studies. Issue 132 (August 2016) - Genolini, Christophe; Lacombe, Amandine; Écochard, René; Subtil, Fabien
Highlights: We present CopyMean, a new method to impute monotone missing values in longitudinal studies. We compare four version of this new method to 12 classical imputation methods. For that, we generate some artificial missing values on real dataset that had initially no missing data. CopyMean outperforms the other methods in many situations (34 on 54). Abstract: 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.