Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning

• 成果类型: Article
• 署名作者: Luckett, Daniel J.; Laber, Eric B.; Kahkoska, Anna R.; Maahs, David M.; Mayer-Davis, Elizabeth; Kosorok, Michael R.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1537919
• 发表日期: 2020
• 页码: 692-706
• 关键词: loop insulin delivery; glycemic control; type-1; adolescents; SYSTEM; predictors; CHILDREN; DESIGN; models; trials

摘要

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.