A comparison of multiple imputation methods for missing data in longitudinal studies 01 Mathematical Sciences

MH Huque; JB Carlin; JA Simpson; KJ Lee

Journal article

A comparison of multiple imputation methods for missing data in longitudinal studies 01 Mathematical Sciences

MH Huque, JB Carlin, JA Simpson, KJ Lee

BMC Medical Research Methodology | BMC | Published : 2018

DOI: 10.1186/s12874-018-0615-6

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Abstract

Background: Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data. Method: Using both empirical data and a simulation study base..

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University of Melbourne Researchers

John Carlin Author

Julie Simpson Author

Kate Lee Author

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Awarded by National Health and Medical Research Council

Funding Acknowledgements

This work was supported by funding from the National Health and Medical Research Council: Project grant ID#1102468, Career Development Fellowship ID#1127984 (KJL), Senior Research Fellowship ID#1104975 (JAS) and Centre of Research Excellence grant ID#1035261, for the Victorian Centre for Biostatistics (ViCBiostat). Research at the Murdoch Childrens Research Institute is supported by the Victorian Government's Operational Infrastructure Support Program. The funding body does not have any roles in the collection, analysis, interpretation and writing the manuscript.