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: 1.1.0
Depends: R (≥ 3.2.0)
Imports: glmnet, DynTxRegime, modelObj
Suggests: dplyr, knitr, rmarkdown
Published: 2020-03-20
DOI: 10.32614/CRAN.package.DevTreatRules
Author: Jeremy Roth [cre, aut], Noah Simon [aut]
Maintainer: Jeremy Roth <jhroth at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: DevTreatRules results


Reference manual: DevTreatRules.pdf
Vignettes: DevTreatRules


Package source: DevTreatRules_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): DevTreatRules_1.1.0.tgz, r-oldrel (arm64): DevTreatRules_1.1.0.tgz, r-release (x86_64): DevTreatRules_1.1.0.tgz, r-oldrel (x86_64): DevTreatRules_1.1.0.tgz
Old sources: DevTreatRules archive


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