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.

Bogni, S., Slanzi, D., Borrotti, M. (2021). Q-learning estimation techniques for Dynamic Treatment Regime. In Book of short papers - SIS 2021 (pp.578-583). Pearson.

Q-learning estimation techniques for Dynamic Treatment Regime

Borrotti, M
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.
slide + paper
dynamic treatment regime, Q-learning, sequential advantage selection, non-linear models
English
50th Scientific Meeting of the Italian Statistical Society
2021
Perna, C; Salvati, N; Schirripa Spagnolo, F;
Book of short papers - SIS 2021
9788891927361
2021
578
583
https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Università/pearson-sis-book-2021-parte-1.pdf
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Bogni, S., Slanzi, D., Borrotti, M. (2021). Q-learning estimation techniques for Dynamic Treatment Regime. In Book of short papers - SIS 2021 (pp.578-583). Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/399795
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