Q-learning estimation techniques for Dynamic Treatment Regime

Optimal individualized treatment estimation in single or multi-stage clinical trials is a breakthrough for personalized medicine. In this context, statistical methodologies can significantly improve the estimation over model-based methods. Furthermore it can help in selecting important variables in high-dimensional settings. In this work, we investigate the performance of an hybrid approach that combine Sequential Advantage Selection (SAS) method and Q-learning. In addition, Q-functions are estimated with linear regression model, random forest and neural network.