Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning

Abstract

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible healthcare for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient’s health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an outpatient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.

Keywords:

• Markov decision processes
• Precision medicine
• Reinforcement learning
• Type 1 diabetes

Acknowledgments

We thank the editor, associate editor, and reviewers for helpful comments which led to a significantly improved paper.

Additional information

Funding

The authors gratefully acknowledge NIH P01 CA142538, NIH UL1 TR001111, NIH T32 CA201159, NSF DMS-1555141, NSF DMS-1513579, NSF DMS-1407732, NIH R01 AA023187.