|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|
|Date Published||2014 Nov|
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.
|Alternate Journal||Stat Sci|
|Original Publication||Q- and A-learning methods for estimating optimal dynamic treatment regimes.|
|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
Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.