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

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 me

SUBMITTER: Resche-Rigon M 

PROVIDER: S-EPMC5496677 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature