论文标题
最佳个性化剂量间隔的政策学习
Policy Learning for Optimal Individualized Dose Intervals
论文作者
论文摘要
我们使用观察数据研究学习个性化剂量间隔的问题。以前几乎没有持续治疗的政策学习作品，所有这些工作都集中在推荐最佳剂量而不是最佳剂量间隔上。在本文中，我们提出了一种估计这种最佳剂量间隔的新方法，称为概率剂量间隔（PDI）。 PDI中剂量的潜在结果比给定概率（例如50％）的预先指定的阈值更好。相关的非convex优化问题可以通过互差函数（DC）算法有效解决。我们证明我们的估计政策是一致的，其风险以根N速度融合了一流的政策。数值模拟显示了所提出的方法比基于结果建模的基准测试的优点。我们进一步证明了我们在确定老年糖尿病患者个性化血红蛋白A1C（HBA1C）对照间隔时的性能。
We study the problem of learning individualized dose intervals using observational data. There are very few previous works for policy learning with continuous treatment, and all of them focused on recommending an optimal dose rather than an optimal dose interval. In this paper, we propose a new method to estimate such an optimal dose interval, named probability dose interval (PDI). The potential outcomes for doses in the PDI are guaranteed better than a pre-specified threshold with a given probability (e.g., 50%). The associated nonconvex optimization problem can be efficiently solved by the Difference-of-Convex functions (DC) algorithm. We prove that our estimated policy is consistent, and its risk converges to that of the best-in-class policy at a root-n rate. Numerical simulations show the advantage of the proposed method over outcome modeling based benchmarks. We further demonstrate the performance of our method in determining individualized Hemoglobin A1c (HbA1c) control intervals for elderly patients with diabetes.
