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autoCovariateSelection  

Automated Covariate Selection Using HDPS Algorithm
View on CRAN: Click here


Download and install autoCovariateSelection package within the R console
Install from CRAN:
install.packages("autoCovariateSelection")

Install from Github:
library("remotes")
install_github("cran/autoCovariateSelection")

Install by package version:
library("remotes")
install_version("autoCovariateSelection", "1.0.0")



Attach the package and use:
library("autoCovariateSelection")
Maintained by
Dennis Robert
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-12-14
Latest Update: 2020-12-14
Description:
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) .
How to cite:
Dennis Robert (2020). autoCovariateSelection: Automated Covariate Selection Using HDPS Algorithm. R package version 1.0.0, https://cran.r-project.org/web/packages/autoCovariateSelection. Accessed 21 Nov. 2024.
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Complete documentation for autoCovariateSelection
Functions, R codes and Examples using the autoCovariateSelection R package
Some associated functions: get_candidate_covariates . get_prioritised_covariates . get_recurrence_covariates . get_relative_risk . rwd . 
Some associated R codes: acs.R . data.R .  Full autoCovariateSelection package functions and examples
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