The R Journal: accepted article

This article will be copy edited and may be changed before publication.

rollmatch: An R Package for Rolling Entry Matching PDF download
Kasey Jones, Rob Chew, Allison Witman and Yiyan Liu

Abstract The gold standard of experimental research is the randomized control trial. However, interventions are often implemented without a randomized control group for practical or ethical reasons. Propensity score matching (PSM) is a popular method for minimizing the effects of a randomized experiment from observational data by matching members of a treatment group to similar candidates that did not receive the intervention. Traditional PSM is not designed for studies that enroll participants on a rolling basis and does not provide a solution for interventions in which the baseline and intervention period are undefined in the comparison group. Rolling Entry Matching (REM) is a new matching method that addresses both issues. REM selects comparison members who are similar to intervention members with respect to both static (e.g., race) and dynamic (e.g., health conditions) characteristics. This paper will discuss the key components of REM and introduce the rollmatch R package.

Received: 2018-05-29; online 2019-07-30, supplementary material, (1.1 Kb)
CRAN packages: rollmatch, CBPS, ipw, MatchIt, Matching, optmatch
CRAN Task Views implied by cited CRAN packages: SocialSciences, HighPerformanceComputing, MissingData, OfficialStatistics, Optimization


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

@article{RJ-2019-005,
  author = {Kasey Jones and Rob Chew and Allison Witman and Yiyan Liu},
  title = {{rollmatch: An R Package for Rolling Entry Matching}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-005},
  url = {https://journal.r-project.org/archive/2019/RJ-2019-005/index.html}
}