@inproceedings{ac56f0bb0bbc4cb2917fe8802aa89036,
title = "PsmPy: A Package for Retrospective Cohort Matching in Python",
abstract = "Propensity score matching (PSM) is a technique used in retrospective investigation of cohort matching as an alternative approach to the prospective matching that is typically used by a randomized control trial (RCT). The process of selecting untreated cases that are the best match to the treated cases is the focus of this research. We created a PSM package for the python environment, termed PsmPy, to carry out this task. The PsmPy package debuted and proposed here is based on a logistic regression logit score where a match is selected using k-nearest neighbors (k-NN). Additional plotting and arguments are available to the user and are also described. To benchmark our method, we compared it with the existing R package, MatchIt, and evaluated our covariates' residual effect sizes with respect to the treatment condition before and after matching. Using a Mann-Whitney statistical test, we showed that our method significantly outperformed MatchIt in cohort matching (U=49, p<0.0001) when comparing residual effect sizes of the covariates. The PsmPy demonstrated a 10-fold average improvement in residual effect sizes amongst covariates when compared with the package MatchIt, suggesting that it is a viable alternative for use in propensity matching studies.",
keywords = "COVID-19, MatchIt, PsmPy, Python, balance, causal inference, propensity score matching",
author = "Adrienne Kline and Yuan Luo",
note = "Funding Information: ACKNOWLEDGMENT The study is supported in part by NIH Grants U01TR003528 and R01LM013337. Publisher Copyright: {\textcopyright} 2022 IEEE.; 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 ; Conference date: 11-07-2022 Through 15-07-2022",
year = "2022",
doi = "10.1109/EMBC48229.2022.9871333",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1354--1357",
booktitle = "44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022",
address = "United States",
}