DTRlearn2

Statistical Learning Methods for Optimizing Dynamic Treatment Regimes (DTRs)

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.

Reference

A simple example of a 2-stage SMART: children with ADHD are randomized at each treatemnt decision time point

Statistical learning methods implemented in this pacakge and their performance in learning a 4-stage DTR

Installation in R

install.packages("devtools")
library(devtools)
install_github("ychen178/DTRlearn2")