期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 27, 期 6, 页码 1634-1649出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280216666564
关键词
Missing data; multilevel model; multiple imputation; chained equations; fully conditional specification; individual patient data meta-analysis
类别
资金
- Global Research on Acute Conditions Team (GREAT)
- Agence Nationale de Securite du Medicament et des Produits de Sante
- Medical Research Council [U105260558]
- Medical Research Council [MC_U105260558] Funding Source: researchfish
- MRC [MC_U105260558] Funding Source: UKRI
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.
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