Abstract
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. Copyright © 2010 John Wiley & Sons, Ltd.
Keywords
Imputation (statistics)Categorical variableComputer scienceMissing dataData miningMachine learning
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Publication Info
- Year
- 2010
- Type
- article
- Volume
- 30
- Issue
- 4
- Pages
- 377-399
- Citations
- 8772
- Access
- Closed
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Cite This
Ian R. White,
Patrick Royston,
Angela Wood
(2010).
Multiple imputation using chained equations: Issues and guidance for practice.
Statistics in Medicine
, 30
(4)
, 377-399.
https://doi.org/10.1002/sim.4067
Identifiers
- DOI
- 10.1002/sim.4067