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arXiv · 2107.07875

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

Palash Ghosh·Xinru Wang·Trikay Nalamada·Shruti Agarwal·Maria Jahja·Bibhas Chakraborty
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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 under which Q-shared fails. We develop 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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Palash Ghosh, Xinru Wang, Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty. 2024-12-04. A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens. https://arxiv.org/abs/2107.07875

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