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Multiple imputation by chained equations for systematically and sporadically missing multilevel data

Resche-Rigon, M; White, IR; (2018) Multiple imputation by chained equations for systematically and sporadically missing multilevel data. Statistical Methods in Medical Research , 27 (6) pp. 1634-1649. 10.1177/0962280216666564. Green open access

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Abstract

In multilevel settings such as individual participant data meta-analysis, a variable is ‘systematically missing’ if it is wholly missing in some clusters and ‘sporadically missing’ if it is partly missing in some clusters. Previously proposed methods to impute incomplete multilevel data handle either systematically or sporadically missing data, but frequently both patterns are observed. We describe a new multiple imputation by chained equations (MICE) algorithm for multilevel data with arbitrary patterns of systematically and sporadically missing variables. The algorithm is described for multilevel normal data but can easily be extended for other variable types. We first propose two methods for imputing a single incomplete variable: an extension of an existing method and a new two-stage method which conveniently allows for heteroscedastic data. We then discuss the difficulties of imputing missing values in several variables in multilevel data using MICE, and show that even the simplest joint multilevel model implies conditional models which involve cluster means and heteroscedasticity. However, a simulation study finds that the proposed methods can be successfully combined in a multilevel MICE procedure, even when cluster means are not included in the imputation models.

Type: Article
Title: Multiple imputation by chained equations for systematically and sporadically missing multilevel data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0962280216666564
Publisher version: https://doi.org/10.1177%2F0962280216666564
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Missing data, multilevel model, multiple imputation, chained equations, fully conditional specification, individual patient data meta-analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
URI: https://discovery.ucl.ac.uk/id/eprint/10064556
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