|Title||Dynamic treatment regimes: technical challenges and applications.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Laber, Eric B., Daniel J. Lizotte, Min Qian, William E. Pelham, and Susan A. Murphy|
|Journal||Electron J Stat|
Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention. Each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review a critical inferential challenge that results from nonregularity, which often arises in this area. In particular, nonregularity arises in inference for parameters in the optimal dynamic treatment regime; the asymptotic, limiting, distribution of estimators are sensitive to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.
|Alternate Journal||Electron J Stat|
|Original Publication||Dynamic treatment regimes: technical challenges and applications.|
|PubMed Central ID||PMC4209714|
|Grant List||R01 MH099898 / MH / NIMH NIH HHS / United States |
R01 HD073975 / HD / NICHD NIH HHS / United States
P50 DA010075 / DA / NIDA NIH HHS / United States
U54 EB020404 / EB / NIBIB NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 MH080015 / MH / NIMH NIH HHS / United States
Dynamic treatment regimes: technical challenges and applications.