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.
Cite this paper
C. Genolini, R. Écochard and H. Jacqmin-Gadda, "Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies," Open Journal of Statistics, Vol. 3 No. 4, 2013, pp. 26-40. doi: 10.4236/ojs.2013.34A004.
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