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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-063.zip
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For attribution, please cite this work as
Aikens, et al., "stratamatch: Prognostic Score Stratification Using a Pilot Design", The R Journal, 2021
BibTeX citation
@article{RJ-2021-063,
author = {Aikens, Rachael C. and Rigdon, Joseph and Lee, Justin and Baiocchi, Michael and Goldstone, Andrew B. and Chiu, Peter and Woo, Y. Joseph and Chen, Jonathan H.},
title = {stratamatch: Prognostic Score Stratification Using a Pilot Design},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-063},
doi = {10.32614/RJ-2021-063},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {614-630}
}