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. 2018 Feb 23;6(1):e11. doi: 10.2196/medinform.8960

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©Brett K Beaulieu-Jones, Daniel R Lavage, John W Snyder, Jason H Moore, Sarah A Pendergrass, Christopher R Bauer. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.02.2018.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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Assessment of multiple imputation for each method. Using simulation 4, missing values were imputed multiple times with each method. The x-axes show the root mean square error (RMSE) between the imputed data and the observed values. The y-axes show the RMSE between multiple imputations of the same data. The axis scales vary between panels to better show the range of variation. The laboratory tests are indicated by the color of the points. The black diagonal line represents unity (y=x). Panels are ordered by each method’s mean deviation (MD) from unity, indicated in the top left corner of each panel. In the last 7 panels, the unity line is not visible because the variation between multiple imputations was close to zero. FI: fancyimpute; KNN: k-nearest neighbors; MICE: Multivariate Imputation by Chained Equations; pmm: predictive mean matching; RF: random forest; SVD: singular value decomposition.