Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

ABSTRACT: Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving features and intermediate outcomes at each treatment stage. Patient heterogeneity and the complexity and chronicity of many diseases call for learning optimal DTRs that can best tailor treatment according to each individual's time-varying characteristics (eg, intermediate response over time). In this paper, we propose a robust and efficient approach referred to as Augmented Outcome-weighted Learning (AOL) to identify optimal DTRs from sequential multiple assignment randomized trials. We improve previously proposed outcome-weighted learning to allow for negative weights. Furthermore, to reduce the variability of weights for numeric stability and improve estimation accuracy, in AOL, we propose

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6191367 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature