Journal article

Introduction to multiple imputation for dealing with missing data

Katherine J Lee, Julie A Simpson

RESPIROLOGY | WILEY | Published : 2014

Abstract

Missing data are common in both observational and experimental studies. Multiple imputation (MI) is a two-stage approach where missing values are imputed a number of times using a statistical model based on the available data and then inference is combined across the completed datasets. This approach is becoming increasingly popular for handling missing data. In this paper, we introduce the method of MI, as well as a discussion surrounding when MI can be a useful method for handling missing data and the drawbacks of this approach. We illustrate MI when exploring the association between current asthma status and forced expiratory volume in 1 s after adjustment for potential confounders using ..

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Grants

Awarded by NHMRC


Awarded by National Health and Medical Research Council, Centre of Research Excellence


Funding Acknowledgements

We thank the TAHS Steering Committee for providing us with a random subset of the data from the fifth decade of follow up of the TAHS cohort which was funded by the NHMRC (ID 299901). This work was supported by funding from the National Health and Medical Research Council: Career Development Fellowship ID 1053609 (K.J.L.), a Centre of Research Excellence grant, ID 1035261, awarded to the Victorian Centre for Biostatistics (ViCBiostat), and project grant 607400. Research at the Murdoch Childrens Research Institute is supported by the Victorian Government's Operational Infrastructure Support Program.