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Longitudinal studies are those in which the same variable isrepeatedly measured at different times. These studies are more likely thanothers to suffer from missing values. Since the presence of missing values mayhave an important impact on statistical analyses, it is important that they should be dealtwith properly. In this paper, we present -Copy Mean-, a new method toimpute intermittent missing values. We compared its efficiency in elevenimputation methods dedicated to the treatment of missing values in longitudinaldata. All these methods were tested on three markedly different real datasetsstationary, increasing, and sinusoidal pattern with complete data. For eachof 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 atRandom, or a Missing Not at Random missingness mechanism. Our results show thatCopy Mean has a great effectiveness, exceeding or equaling the performance ofother methods in almost all configurations. The effectiveness of linearinterpolation is highly data-dependent. The Last Occurrence Carried Forwardmethod is strongly discouraged.

KEYWORDS

Imputation; Longitudinal Data; Intermittent Missing Values

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. 4A, 2013, pp. 26-40. doi: 10.4236-ojs.2013.34A004.





Autor: Christophe Genolini, René Écochard, Hélène Jacqmin-Gadda

Fuente: http://www.scirp.org/



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