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. 2016 Jan;4(2):30.
doi: 10.3978/j.issn.2305-5839.2015.12.63.

Multiple imputation with multivariate imputation by chained equation (MICE) package

Zhongheng Zhang  1
Affiliations

Multiple imputation with multivariate imputation by chained equation (MICE) package

Zhongheng Zhang. Ann Transl Med. 2016 Jan.

Abstract

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.

Keywords: Big-data clinical trial; R; imputed complete dataset; multiple imputation (MI); multivariate imputation by chained equation (MICE) package.

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Conflict of interest statement

Conflicts of Interest: The author has no conflicts of interest to declare.

Figures

None
Zhongheng Zhang, MMed.
Figure 1
Figure 1
Schematic illustration of how MICE package works with data frame with missing values. Note the sequential use of mice(), with() and pool() functions.
See this image and copyright information in PMC

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