CRAN

DevTreatRules 1.0.0

Develop Treatment Rules with Observational Data

Released May 16, 2019 by Jeremy Roth

This package cannot yet be used with Renjin it depends on other packages which are not available: DynTxRegime 4.1 and glmnet 2.0-18

Dependencies

glmnet 2.0-18 DynTxRegime 4.1 modelObj 4.0

Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) ; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) . Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.

Source

R

View GitHub Mirror

Release History