DevTreatRules: Develop Treatment Rules with Observational Data

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) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. 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.

Version: 1.0.0
Depends: R (≥ 3.2.0)
Imports: glmnet, DynTxRegime, modelObj
Suggests: dplyr, knitr, rmarkdown
Published: 2019-05-16
Author: Jeremy Roth [cre, aut], Noah Simon [aut]
Maintainer: Jeremy Roth <jhroth at uw.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: DevTreatRules results

Downloads:

Reference manual: DevTreatRules.pdf
Vignettes: DevTreatRules
Package source: DevTreatRules_1.0.0.tar.gz
Windows binaries: r-devel: DevTreatRules_1.0.0.zip, r-release: DevTreatRules_1.0.0.zip, r-oldrel: DevTreatRules_1.0.0.zip
OS X binaries: r-release: DevTreatRules_1.0.0.tgz, r-oldrel: DevTreatRules_1.0.0.tgz

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