|Title||Penalized Q-Learning for Dynamic Treatment Regimens.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Song, R, W Wang, D Zeng, and M R. Kosorok|
|Date Published||2015 Jul|
A dynamic treatment regimen incorporates both accrued information and long-term effects of treatment from specially designed clinical trials. As these trials become more and more popular in conjunction with longitudinal data from clinical studies, the development of statistical inference for optimal dynamic treatment regimens is a high priority. In this paper, we propose a new machine learning framework called penalized Q-learning, under which valid statistical inference is established. We also propose a new statistical procedure: individual selection and corresponding methods for incorporating individual selection within penalized Q-learning. Extensive numerical studies are presented which compare the proposed methods with existing methods, under a variety of scenarios, and demonstrate that the proposed approach is both inferentially and computationally superior. It is illustrated with a depression clinical trial study.
|Alternate Journal||Stat Sin|
|Original Publication||Penalized Q-Learning for dynamic treatment regimens.|
|PubMed Central ID||PMC4526274|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Penalized Q-Learning for Dynamic Treatment Regimens.