Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies
By Christophe Genolini, René Écochard, Hélène Jacqmin-Gadda. 2013
63 citations recorded by the source corpus.
Longitudinal studies are those in which the same variable is repeatedly measured at different times. These studies are more likely than others to suffer from missing values. Since the presence of missing values may have an important impact on statistical analyses, it is important that they should be dealt with properly. In this paper, we present “Copy Mean”, a new method to impute intermittent missing values. We compared its efficiency in eleven imputation methods dedicated to the treatment of missing values in longitudinal data. All these methods were tested on three markedly different real datasets (stationary, increasing, and sinusoidal pattern) with complete data. For each of them, we generated nine types of incomplete datasets that include 10%, 30%, or 50% of missing data using either a Missing Completely at Random, a Missing at Random, or a Missing Not at Random missingness mechanism. Our results show that Copy Mean has a great effectiveness, exceeding or equaling the performance of other methods in almost all configurations. The effectiveness of linear interpolation is highly data-dependent. The Last Occurrence Carried Forward method is strongly discouraged.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/054cbb007bd2ee5e33525c80149a0eea94c4ff56
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In Statistics and Probability
Citations in the corpus
- Missing data imputation: focusing on single imputation. (2016, methodology): “There are also varieties of methods for longitudinal imputation (Table 1) (9-11).”
- kml and kml3d: R Packages to Cluster Longitudinal Data (2015, methodology): “The efficiency of this method has been shown in Genolini et al. (2013a).”