Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.

ABSTRACT: 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 conce

SUBMITTER: Chakraborty B 

PROVIDER: S-EPMC3864701 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature