DevTreatRules (1.0.0)

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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) ; (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.

Maintainer: Jeremy Roth
Author(s): Jeremy Roth [cre, aut], Noah Simon [aut]

License: GPL (>= 2)

Uses: DynTxRegime, glmnet, modelObj, knitr, dplyr, rmarkdown

Released 4 months ago.



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