Journal article

Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

Md Hamidul Huque, Margarita Moreno-Betancur, Matteo Quartagno, Julie A Simpson, John B Carlin, Katherine J Lee

Biometrical Journal | WILEY | Published : 2020

Abstract

Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two general approaches are available in standard computer packages: MI based on the posterior distribution of incomplete variables under a multivariate (joint) model, and fully conditional specification (FCS), which imputes missing values using univariate conditional distributions for each incomplete variable given all the others, cycling iteratively through the univariate imputation models. In the context of longitudinal or clustered data, it is not clear whether these approaches result in consistent estimates of regression coefficient and variance component parameters when the analysis model of interes..

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Grants

Awarded by National Health and Medical Research Council


Awarded by National Health and Medical Research Council: Centre of Research Excellence grant


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.