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Multiple imputation with multivariate imputation by chained equation (MICE) package.
Open accessAbstract
Multiple imputation (MI) is an advanced technique for handing missing values. It is superior to single imputation in that it takes into account uncertainty in missing value imputation. However, MI is underutilized in medical literature due to lack of familiarity and computational challenges. The article provides a step-by-step approach to perform MI by using R multivariate imputation by chained equation (MICE) package. The procedure firstly imputed m sets of complete dataset by calling mice() function. Then statistical analysis such as univariate analysis and regression model can be performed within each dataset by calling with() function. This function sets the environment for statistical analysis. Lastly, the results obtained from each analysis are combined by using pool() function.
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Citations
Cites
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
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“We imputed missing data using predictive mean matching for all numeric variables through the multivariate imputation by chained equations (mice) method [56].”
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Metadata
- Authors
- Zhang, Z.
- Venue
- PubMed
- Publisher
- National Institutes of Health
- Published
- 2016-01-01
- Volume
- 4
- Issue
- 2
- Pages
- 30-30
- OpenAlex
- W2234472895
- Cited by
- 740
- References
- 2
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- green
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