DTRlearn2: Statistical Learning Methods for Optimizing Dynamic Treatment
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.
||kernlab, MASS, Matrix, foreach, glmnet, R (≥ 2.10)
||Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang
||Yuan Chen <irene.yuan.chen at gmail.com>
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