Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the Random Forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far. This paper closes this gap by investigating their validity for inferring mean differences in incompletely observed pairs while opposing them to a recent approach that only works with the given observations at hand.
Our findings indicate that machine learning schemes for (multiply) imputing missing values may inflate type-I-error or result in comparably low power in small to moderate matched pairs, even after modifying the test statistics using Rubin’s multiple imputation rule. In addition to an extensive simulation study, an illustrative data example from a breast cancer gene study has been considered.
The corresponding R-code can be accessed through the authors and the gene expression data can be downloaded at http://www.gdac.broadinstitute.org.
Supplementary data are available at Bioinformatics online.

© The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.