A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens

• URL: http://arxiv.org/abs/2107.07875v1
• Date: Tue, 13 Jul 2021 05:31:14 GMT
• Title: A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens
• Authors: Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty and Palash Ghosh
• Abstract summary: We show that the existing Q-shared algorithm can suffer from non-convergence due to the use of linear models in the Q-learning setup. We give evidence for the proposed method in a real-world application and several synthetic simulations.
• Score: 3.9023554886892438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A dynamic treatment regimen (DTR) is a set of decision rules to personalize treatments for an individual using their medical history. The Q-learning based Q-shared algorithm has been used to develop DTRs that involve decision rules shared across multiple stages of intervention. We show that the existing Q-shared algorithm can suffer from non-convergence due to the use of linear models in the Q-learning setup, and identify the condition in which Q-shared fails. Leveraging properties from expansion-constrained ordinary least-squares, we give a penalized Q-shared algorithm that not only converges in settings that violate the condition, but can outperform the original Q-shared algorithm even when the condition is satisfied. We give evidence for the proposed method in a real-world application and several synthetic simulations.

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