HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES.

TitleHIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES.
Publication TypeJournal Article
Year of Publication2018
AuthorsShi, Chengchun, Alin Fan, Rui Song, and Wenbin Lu
JournalAnn Stat
Volume46
Issue3
Pagination925-957
Date Published2018 Jun
ISSN0090-5364
Abstract

Precision medicine is a medical paradigm that focuses on finding the most effective treatment decision based on individual patient information. For many complex diseases, such as cancer, treatment decisions need to be tailored over time according to patients' responses to previous treatments. Such an adaptive strategy is referred as a dynamic treatment regime. A major challenge in deriving an optimal dynamic treatment regime arises when an extraordinary large number of prognostic factors, such as patient's genetic information, demographic characteristics, medical history and clinical measurements over time are available, but not all of them are necessary for making treatment decision. This makes variable selection an emerging need in precision medicine. In this paper, we propose a penalized multi-stage A-learning for deriving the optimal dynamic treatment regime when the number of covariates is of the non-polynomial (NP) order of the sample size. To preserve the double robustness property of the A-learning method, we adopt the Dantzig selector which directly penalizes the A-leaning estimating equations. Oracle inequalities of the proposed estimators for the parameters in the optimal dynamic treatment regime and error bounds on the difference between the value functions of the estimated optimal dynamic treatment regime and the true optimal dynamic treatment regime are established. Empirical performance of the proposed approach is evaluated by simulations and illustrated with an application to data from the STAR*D study.

DOI10.1214/17-AOS1570
Alternate JournalAnn Stat
Original PublicationHigh-dimensional A-learning for optimal dynamic treatment regimes
PubMed ID29805186
PubMed Central IDPMC5966293
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
Project: