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.


Reference manual

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1.1 by Yuan Chen, a year ago

Browse source code at

Authors: Yuan Chen , Ying Liu , Donglin Zeng , Yuanjia Wang

Documentation:   PDF Manual  

GPL-2 license

Depends on kernlab, MASS, Matrix, foreach, glmnet

See at CRAN