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)