Journal article

Multiple imputation in the presence of non-normal data

Katherine J Lee, John B Carlin



Multiple imputation (MI) is becoming increasingly popular for handling missing data. Standard approaches for MI assume normality for continuous variables (conditionally on the other variables in the imputation model). However, it is unclear how to impute non-normally distributed continuous variables. Using simulation and a case study, we compared various transformations applied prior to imputation, including a novel non-parametric transformation, to imputation on the raw scale and using predictive mean matching (PMM) when imputing non-normal data. We generated data from a range of non-normal distributions, and set 50% to missing completely at random or missing at random. We then imputed miss..

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Awarded by National Health and Medical Research Council

Awarded by Centre of Research Excellence grant

Funding Acknowledgements

This work was supported by funding from the National Health and Medical Research Council: Career Development Fellowship ID#1053609 (KJL) and the 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.