期刊
STATISTICS IN MEDICINE
卷 -, 期 -, 页码 -出版社
WILEY
DOI: 10.1002/sim.9608
关键词
causal inference; double robustness; dynamic treatment regimens; inverse probability weighting; personalized medicine; precision medicine
类别
资金
- Fonds de Recherche du Quebec - Sante
- Natural Sciences and Engineering Research Council of Canada [265385]
- [2016-06295]
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.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据