Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.

TitleQ- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.
Publication TypeJournal Article
Year of Publication2014
AuthorsSchulte, Phillip J., Anastasios A. Tsiatis, Eric B. Laber, and Marie Davidian
JournalStat Sci
Volume29
Issue4
Pagination640-661
Date Published2014 Nov
ISSN0883-4237
Abstract

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. - and -learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.

DOI10.1214/13-STS450
Alternate JournalStat Sci
Original PublicationQ- and A-learning methods for estimating optimal dynamic treatment regimes.
PubMed ID25620840
PubMed Central IDPMC4300556
Grant ListT32 HL079896 / HL / NHLBI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 CA051962 / CA / NCI NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: