Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and reducing the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, to the best of our knowledge, there is no comprehensive matching and weighting software that can be easily implemented with multiply imputed datasets. In this paper, we review this problem and suggest a framework to map out the matching and weighting of multiply imputed datasets to 5 actions as well as the best practices to assess balance in these datasets after matching and weighting. We also illustrate these approaches using a companion package for R, MatchThem.
MatchThem, MatchIt, WeightIt, cobalt, mice, Amelia, survey
OfficialStatistics, SocialSciences, MissingData, Multivariate, Survival
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For attribution, please cite this work as
Pishgar, et al., "MatchThem:: Matching and Weighting after Multiple Imputation", The R Journal, 2021
BibTeX citation
@article{RJ-2021-073,
author = {Pishgar, Farhad and Greifer, Noah and Leyrat, Clémence and Stuart, Elizabeth},
title = {MatchThem:: Matching and Weighting after Multiple Imputation},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-073},
doi = {10.32614/RJ-2021-073},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {292-305}
}