用于个性化剂量发现的核辅助学习
Kernel Assisted Learning for Personalized Dose Finding
摘要：
个体化剂量规则基于患者水平信息（例如身体状况、遗传因素和用药史）推荐连续安全剂量范围内的剂量水平。传统上，个性化剂量发现过程需要患者的重复临床访问和剂量的频繁调整。因此，患者在该过程中不断暴露于剂量不足和剂量过量的风险。采用统计学方法寻找最佳的个体化给药规则可以降低患者的成本和风险。在这篇文章中，我们提出了一个核辅助学习方法估计最佳的个性化剂量规则。所提出的方法也可以适用于所有其他连续决策问题。所提出的方法的优点包括鲁棒性模型误设定和提供统计推断的估计参数的能力。在模拟研究中，我们表明，这种方法是能够确定最佳的个性化剂量规则，并产生良好的预期结果的人口。最后，我们说明了我们的方法使用数据华法林剂量研究血栓形成患者。
英文摘要：
An individualized dose rule recommends a dose level within a continuous safe dose range based on patient level information such as physical conditions, genetic factors and medication histories. Traditionally, personalized dose finding process requires repeating clinical visits of the patient and frequent adjustments of the dosage. Thus the patient is constantly exposed to the risk of underdosing and overdosing during the process. Statistical methods for finding an optimal individualized dose rule can lower the costs and risks for patients. In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule. The proposed methodology can also be applied to all other continuous decision-making problems. Advantages of the proposed method include robustness to model misspecification and capability of providing statistical inference for the estimated parameters. In the simulation studies, we show that this method is capable of identifying the optimal individualized dose rule and produces favorable expected outcomes in the population. Finally, we illustrate our approach using data from a warfarin dosing study for thrombosis patients.
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