We provide a comprehensive software to estimate general K-stage DTRs from sequential multiple assignment randomization trials (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.
Yuan Chen, Ying Liu, Donglin Zeng, and Yuanjia Wang (2020). Statistical Learning Methods for Optimizing Dynamic Treatment Regimes in Subgroup Identification. In Design and Analysis of Subgroups with Biopharmaceutical Applications. Chpater 11. Springer. Edited by Naitee Ting, Joseph C. Cappelleri,Shuyen Ho, and Ding-Geng Chen.
CRAN: https://CRAN.R-project.org/package=DTRlearn2
install.packages("devtools")
library(devtools)
install_github("ychen178/DTRlearn2")