Multiple imputation using chained equations: Issues and guidance for practice
Ian R. WhiteMRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.. [email protected]Patrick RoystonHub for Trials Methodology Research, MRC Clinical Trials Unit and University College London, 222 Euston Road, London NW1 2DA, U.KAngela WoodDepartment of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB2 8RN, U.K
2010en
ABI
Abstract
Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments.
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