Wednesday 19 July 2 p.m. - 3:40 p.m. (Canada/Eastern)
Abstract
G-estimation and dynamic weighted ordinary least squares are double robust methods to identify optimal adaptive treatment strategies. It is underappreciated that they require modeling all existing treatment-confounder interactions to be consistent. Identifying optimal partially adaptive treatment strategies that tailor treatments according to only a few covariates, ignoring some interactions, may be preferable in practice. Building on G-estimation and dWOLS, we propose estimators of such partially adaptive strategies and demonstrate their double robustness. We investigate these estimators in a simulation study and compare them with alternatives. We also investigate if there is are benefits in terms of statistical efficiency and distribution of the value of the estimated optimal strategy when estimating an optimal partially adaptive treatment strategy instead of an optimal adaptive treatment strategy. We illustrate the proposed methods in an application where we estimate an optimal partially adaptive treatment strategy for tailoring hormonal therapy use in breast cancer patients.
