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May 2018 Multiple Imputation for Multilevel Data with Continuous and Binary Variables
Vincent Audigier, Ian R. White, Shahab Jolani, Thomas P. A. Debray, Matteo Quartagno, James Carpenter, Stef van Buuren, Matthieu Resche-Rigon
Statist. Sci. 33(2): 160-183 (May 2018). DOI: 10.1214/18-STS646


We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data.


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Vincent Audigier. Ian R. White. Shahab Jolani. Thomas P. A. Debray. Matteo Quartagno. James Carpenter. Stef van Buuren. Matthieu Resche-Rigon. "Multiple Imputation for Multilevel Data with Continuous and Binary Variables." Statist. Sci. 33 (2) 160 - 183, May 2018.


Published: May 2018
First available in Project Euclid: 3 May 2018

zbMATH: 1397.62265
MathSciNet: MR3797708
Digital Object Identifier: 10.1214/18-STS646

Keywords: fully conditional specification , joint modelling , missing data , mixed data , multilevel data , multiple imputation , systematically missing values

Rights: Copyright © 2018 Institute of Mathematical Statistics


Vol.33 • No. 2 • May 2018
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