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
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
Materials:	NEWS
CRAN checks:	DevTreatRules results

Documentation:

Reference manual:	DevTreatRules.pdf
Vignettes:	DevTreatRules

Downloads:

Package source:	DevTreatRules_1.1.0.tar.gz
Windows binaries:	r-devel: DevTreatRules_1.1.0.zip, r-release: DevTreatRules_1.1.0.zip, r-oldrel: DevTreatRules_1.1.0.zip
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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