Doubly Robust Estimation of Optimal Dosing Strategies

The goal of precision medicine is to tailor treatment strategies on an individual patient level. Although several estimation techniques have been developed for determining optimal treatment rules, the majority of methods focus on the case of a dichotomous treatment, an example being the dynamic weighted ordinary least squares regression approach of Wallace and Moodie. We propose an extension to the aforementioned framework to allow for a continuous treatment with the ultimate goal of estimating optimal dosing strategies. The proposed method is shown to be doubly robust against model misspecification whenever the implemented weights satisfy a particular balancing condition. A broad class of weight functions can be derived from the balancing condition, providing a flexible regression based estimation method in the context of adaptive treatment strategies for continuous valued treatments. Supplementary materials for this article are available online.

精准医学（precision medicine）的目标是针对个体患者定制治疗方案。尽管目前已开发出多种用于确定最优治疗规则的估计技术，但大多数方法均聚焦于二分类治疗场景，例如Wallace与Moodie提出的动态加权普通最小二乘回归（dynamic weighted ordinary least squares regression）方法。本文提出对上述框架进行扩展，以支持连续治疗场景，最终实现最优给药策略的估计。研究表明，当所用权重满足特定平衡条件时，所提方法对模型误设具有双重稳健性（doubly robust）。可从该平衡条件推导出一类广泛的权重函数，从而为连续型治疗的自适应治疗策略场景提供一种灵活的基于回归的估计方法。本文补充材料可在线获取。