@article{ATM22865,
author = {Zhongheng Zhang and Hwa Jung Kim and Guillaume Lonjon and Yibing Zhu and written on behalf of AME Big-Data Clinical Trial Collaborative Group},
title = {Balance diagnostics after propensity score matching},
journal = {Annals of Translational Medicine},
volume = {7},
number = {1},
year = {2018},
keywords = {},
abstract = {Propensity score matching (PSM) is a popular method in clinical researches to create a balanced covariate distribution between treated and untreated groups. However, the balance diagnostics are often not appropriately conducted and reported in the literature and therefore the validity of the findings from the PSM analysis is not warranted. The special article aims to outline the methods used for assessing balance in covariates after PSM. Standardized mean difference (SMD) is the most commonly used statistic to examine the balance of covariate distribution between treatment groups. Because SMD is independent of the unit of measurement, it allows comparison between variables with different unit of measurement. SMD can be reported with plot. Variance is the second central moment and should also be compared in the matched sample. Finally, a correct specification of the propensity score model (e.g., linearity and additivity) should be re-assessed if there is evidence of imbalance between treated and untreated. R code for the implementation of balance diagnostics is provided and explained.},
issn = {2305-5847}, url = {https://atm.amegroups.org/article/view/22865}
}