A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.

ABSTRACT: Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF)

SUBMITTER: Brown DW 

PROVIDER: S-EPMC7334100 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature