The R Journal: article published in 2021, volume 13:1

stratamatch: Prognostic Score Stratification Using a Pilot Design PDF download
Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Andrew B. Goldstone, Peter Chiu, Y. Joseph Woo and Jonathan H. Chen , The R Journal (2021) 13:1, pages 614-630.

Abstract Optimal propensity score matching has emerged as one of the most ubiquitous approaches for causal inference studies on observational data. However, outstanding critiques of the statistical properties of propensity score matching have cast doubt on the statistical efficiency of this technique, and the poor scalability of optimal matching to large data sets makes this approach inconvenient if not infeasible for sample sizes that are increasingly commonplace in modern observational data. The stratamatch package provides implementation support and diagnostics for ‘stratified matching designs,’ an approach that addresses both of these issues with optimal propensity score matching for large-sample observational studies. First, stratifying the data enables more computationally efficient matching of large data sets. Second, stratamatch implements a ‘pilot design’ approach in order to stratify by a prognostic score, which may increase the precision of the effect estimate and increase power in sensitivity analyses of unmeasured confounding.

Received: 2021-03-01; online 2021-06-21, supplementary material, (3.3 Kb)
CRAN packages: stratamatch, MatchIt, optmatch, DOS2, nearfar, sensitivitymw, sensitivityfull, glmnet
CRAN Task Views implied by cited CRAN packages: SocialSciences, MachineLearning, OfficialStatistics, Optimization, Survival

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

  author = {Rachael C. Aikens and Joseph Rigdon and Justin Lee and
          Michael Baiocchi and Andrew B. Goldstone and Peter
          Chiu and Y. Joseph Woo and Jonathan H. Chen},
  title = {{stratamatch: Prognostic Score Stratification Using a Pilot
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-063},
  url = {},
  pages = {614--630},
  volume = {13},
  number = {1}