|Title||Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Chakraborty, Bibhas, Eric B. Laber, and Yingqi Zhao|
|Date Published||2013 Sep|
|Keywords||Antidepressive Agents, Artificial Intelligence, Biometry, Computer Simulation, Confidence Intervals, Decision Theory, Depressive Disorder, Major, Humans, Linear Models, Logistic Models, Models, Statistical, Monte Carlo Method, Randomized Controlled Trials as Topic|
A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example.
|Original Publication||Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.|
|PubMed Central ID||PMC3864701|
|Grant List||N01MH90003 / MH / NIMH NIH HHS / United States |
P01 CA142538 / CA / NCI NIH HHS / United States
R01 NS072127 / NS / NINDS NIH HHS / United States
R01 NS072127-01A1 / NS / NINDS NIH HHS / United States
Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.