hdps-package: High-dimensional propensity score algorithm

In lendle/hdps: High-dimensional propensity score algorithm

Description

The high-dimensional propensity score (HDPS) algorithm is a method for high-dimensional proxy adjustment in claims data. This package implements the variable transformation and variable selection parts of the algorithm.

Details

Package:	hdps
Type:	Package
Version:	0.1.6
Date:	2017-08-16
License:	MIT

This package implements part of step 2 (identify_covariates), steps 3 (assess_recurrence) and 4 (prioritize_covariates) of the HDPS algorithm (Schneeweiss et al., 2009).

The hdps_screen function is a wrapper function for identify_covariates, assess_recurrence, and prioritize_covariates.

Author(s)

Sam Lendle

Maintainer: Sam Lendle <[e-mail]>

References

Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., & Brookhart, M. A. (2009). High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology (Cambridge, Mass.), 20(4), 512.

Examples

#~~ simple examples of the most important functions ~~

lendle/hdps documentation built on May 9, 2019, 8:34 a.m.