Package: DevTreatRules 1.1.0

Jeremy Roth

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.

Authors:Jeremy Roth [cre, aut], Noah Simon [aut]

DevTreatRules_1.1.0.tar.gz
DevTreatRules_1.1.0.tar.gz(r-4.5-noble)DevTreatRules_1.1.0.tar.gz(r-4.4-noble)
DevTreatRules_1.1.0.tgz(r-4.4-emscripten)DevTreatRules_1.1.0.tgz(r-4.3-emscripten)
DevTreatRules.pdf |DevTreatRules.html✨
DevTreatRules/json (API)
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5 exports 0.00 score 15 dependencies 2 scripts 181 downloads

Last updated 5 years agofrom:97f7d1442d. Checks:OK: 2. Indexed: yes.

Exports:BuildRuleCompareRulesOnValidationEvaluateRulePredictRuleSplitData

Dependencies:codetoolsdfoptimDynTxRegimeforeachglmnetiteratorskernlablatticeMatrixmodelObjRcppRcppEigenrgenoudshapesurvival