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

Published

Journal Article

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. Q- and A-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.

Full Text

Duke Authors

Cited Authors

  • Schulte, PJ; Tsiatis, AA; Laber, EB; Davidian, M

Published Date

  • November 2014

Published In

Volume / Issue

  • 29 / 4

Start / End Page

  • 640 - 661

PubMed ID

  • 25620840

Pubmed Central ID

  • 25620840

Electronic International Standard Serial Number (EISSN)

  • 2168-8745

International Standard Serial Number (ISSN)

  • 0883-4237

Digital Object Identifier (DOI)

  • 10.1214/13-STS450

Language

  • eng