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.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=DTRlearn2
to link to this page.