autoCovariateSelection - R Package to Implement Automated Covariate Selection for Two
Exposure Cohorts Using High-Dimensional Propensity Score
Algorithm
Contains functions to implement automated covariate
selection using methods described in the high-dimensional
propensity score (HDPS) algorithm by Schneeweiss et.al.
Covariate adjustment in real-world-observational-data (RWD) is
important for for estimating adjusted outcomes and this can be
done by using methods such as, but not limited to, propensity
score matching, propensity score weighting and regression
analysis. While these methods strive to statistically adjust
for confounding, the major challenge is in selecting the
potential covariates that can bias the outcomes comparison
estimates in observational RWD (Real-World-Data). This is where
the utility of automated covariate selection comes in. The
functions in this package help to implement the three major
steps of automated covariate selection as described by
Schneeweiss et. al elsewhere. These three functions, in order
of the steps required to execute automated covariate selection
are, get_candidate_covariates(), get_recurrence_covariates()
and get_prioritised_covariates(). In addition to these
functions, a sample real-world-data from publicly available
de-identified medical claims data is also available for running
examples and also for further exploration. The original article
where the algorithm is described by Schneeweiss et.al. (2009)
<doi:10.1097/EDE.0b013e3181a663cc> .
