DTRlearn2: Statistical Learning Methods for Optimizing Dynamic Treatment Regimes

We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.

Getting started

Package details

AuthorYuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang
MaintainerYuan Chen <[email protected]>
Package repositoryView on CRAN
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DTRlearn2 documentation built on Jan. 4, 2019, 1:04 a.m.