The R Journal: accepted article

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MatchThem:: Matching and Weighting after Multiple Imputation PDF download
Farhad Pishgar, Noah Greifer, Clémence Leyrat and Elizabeth Stuart

Abstract 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 to reduce 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 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.

Received: 2020-10-30; online 2021-08-17, supplementary material, (764 bytes)
CRAN packages: MatchThem, MatchIt, WeightIt, cobalt, mice, Amelia, survey
CRAN Task Views implied by cited CRAN packages: OfficialStatistics, SocialSciences, MissingData, Multivariate, Survival

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Farhad Pishgar and Noah Greifer and Clémence Leyrat and
          Elizabeth Stuart},
  title = {{MatchThem:: Matching and Weighting after Multiple Imputation}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-073},
  url = {}