Journal article

Multiple imputation in the presence of an incomplete binary variable created from an underlying continuous variable

Anneke C Grobler, Katherine Lee

BIOMETRICAL JOURNAL | WILEY | Published : 2019

Abstract

Multiple imputation (MI) is used to handle missing at random (MAR) data. Despite warnings from statisticians, continuous variables are often recoded into binary variables. With MI it is important that the imputation and analysis models are compatible; variables should be imputed in the same form they appear in the analysis model. With an encoded binary variable more accurate imputations may be obtained by imputing the underlying continuous variable. We conducted a simulation study to explore how best to impute a binary variable that was created from an underlying continuous variable. We generated a completely observed continuous outcome associated with an incomplete binary covariate that is ..

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Grants

Awarded by National Health and Medical Research Council


Awarded by Australia's National Health & Medical Research Council


Funding Acknowledgements

National Health and Medical Research Council, Grant/Award Numbers: Career Development Fellowships 1127984, Project grant 1102468; Australia's National Health & Medical Research Council, Grant/Award Number: Career Development Fellowships 1127984