近端个体化功能治疗方案
Proximal Individualized Functional Treatment Regimes
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中文总结 AI 辅助
本文针对存在未测量混杂的观测数据，在近端因果推断框架下提出算法估计最优个体化功能治疗方案，经模拟验证后应用于加速度计数据集优化身体活动分布以改善甘油三酯-葡萄糖指数。
中文摘要 AI 辅助
估计个体化治疗方案（ITR）是数据驱动的个性化决策问题（如精准医疗）的基础。现有ITR研究大多聚焦于分类/连续治疗，或假设不存在未测量混杂。本文首次尝试为存在未测量混杂的观测数据估计最优个体化功能治疗方案（IFTR），其中治疗为函数。我们在近端因果推断框架下建立了一类IFTR的识别结果，基于该结果开发了寻找最优IFTR的算法。通过模拟研究验证了所提方法的良好实际性能，并将其应用于美国国家健康与营养检查调查收集的加速度计数据集，以找到最优身体活动分布，实现甘油三酯-葡萄糖指数的最优表现。
英文摘要
Estimating individualized treatment regimes (ITRs) is fundamental in data-driven personalized decision-making problems, such as precision medicine. Most of the ITR literature either focuses on categorical/continuous treatments or assumes no unmeasured confounding. In this paper, we make the first attempt to estimate the optimal individualized functional treatment regime (IFTR) for observational data where the treatment is a function and unmeasured confounding is present. We establish an identification result for a class of IFTRs under the proximal causal inference framework. Based on the identification result, we develop an algorithm of finding the optimal IFTR. The appealing practical performance of the proposed method is demonstrated by a simulation study. The proposed method is applied to an accelerometry dataset collected by the US National Health and Nutrition Examination Survey to find the optimal physical activity distribution for the best of the Triglyceride-Glucose index.
