Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning

• URL: http://arxiv.org/abs/2110.10719v1
• Date: Wed, 20 Oct 2021 18:38:22 GMT
• Title: Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning
• Authors: Wenzhuo Zhou, Ruoqing Zhu and Annie Qu
• Abstract summary: Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. The practical use of mHealth technology raises unique challenges to existing methodologies on learning an optimal dynamic treatment regime. We propose a Proximal Temporal Learning framework to estimate an optimal regime adaptively adjusted between deterministic and sparse policy models.
• Score: 2.0625936401496237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. However, the practical use of mHealth technology raises unique challenges to existing methodologies on learning an optimal dynamic treatment regime. Many mHealth applications involve decision-making with large numbers of intervention options and under an infinite time horizon setting where the number of decision stages diverges to infinity. In addition, temporary medication shortages may cause optimal treatments to be unavailable, while it is unclear what alternatives can be used. To address these challenges, we propose a Proximal Temporal consistency Learning (pT-Learning) framework to estimate an optimal regime that is adaptively adjusted between deterministic and stochastic sparse policy models. The resulting minimax estimator avoids the double sampling issue in the existing algorithms. It can be further simplified and can easily incorporate off-policy data without mismatched distribution corrections. We study theoretical properties of the sparse policy and establish finite-sample bounds on the excess risk and performance error. The proposed method is implemented by our proximalDTR package and is evaluated through extensive simulation studies and the OhioT1DM mHealth dataset.

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