Title | Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Schulte, Phillip J., Anastasios A. Tsiatis, Eric B. Laber, and Marie Davidian |
Journal | Stat Sci |
Volume | 29 |
Issue | 4 |
Pagination | 640-661 |
Date Published | 2014 Nov |
ISSN | 0883-4237 |
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. |
DOI | 10.1214/13-STS450 |
Alternate Journal | Stat Sci |
Original Publication | Q- and A-learning methods for estimating optimal dynamic treatment regimes. |
PubMed ID | 25620840 |
PubMed Central ID | PMC4300556 |
Grant List | T32 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 |
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