Double robust estimation of optimal partially adaptive treatment strategies: An application to breast cancer treatment using hormonal therapy

Precision medicine aims to tailor treatment decisions according to patients' characteristics. 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. Using data maintained by the Centre des Maladies du Sein, we estimate a partially adaptive treatment strategy for tailoring hormonal therapy use in breast cancer patients. R software implementing our estimators is provided.

Automated summary

This study explores the use of partial adaptive strategies for tailoring treatments, proposing estimators based on G-estimation and dynamic weighted ordinary least squares, and demonstrating their double robustness. Through simulation studies and real data, a partial adaptive treatment strategy is provided for breast cancer patients.