Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.

TitleEstimating Dynamic Treatment Regimes in Mobile Health Using V-learning.
Publication TypeJournal Article
Year of Publication2020
AuthorsLuckett, Daniel J., Eric B. Laber, Anna R. Kahkoska, David M. Maahs, Elizabeth Mayer-Davis, and Michael R. Kosorok
JournalJ Am Stat Assoc
Date Published2020

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care 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 out-patient 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.

Alternate JournalJ Am Stat Assoc
Original Publication Estimating dynamic treatment regimes in mobile health using V-learning.
PubMed ID32952236
PubMed Central IDPMC7500510
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 DK108073 / DK / NIDDK NIH HHS / United States