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.

Q-learning Estimation Techniques for Dynamic Treatment Regime

Debora Slanzi;
2021

Abstract

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.
Book of Short Papers SIS 2021
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3742737
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