Dynamic treatment regime (DTR) plays a critical role in precision medicine when assigning patient-specific treatments at multiple stages and optimizing a long term clinical outcome. However, most of existing work about DTRs have been focused on categorical treatment scenarios, instead of continuous treatment options. Also, the performances of regular black-box machine learning methods and regular tree learning methods are lack of interpretability and global optimality respectively. In this paper, we propose a non-greedy global optimization method for dose search, namely Global Optimal Dosage Tree-based learning method (GoDoTree), which combines a robust estimation of the counterfactual outcome mean with an interpretable and non-greedy decision tree for estimating the global optimal dynamic dosage treatment regime in a multiple-stage setting. GoDoTree-Learning recursively estimates how the counterfactual outcome mean depends on a continuous treatment dosage using doubly robust estimators at each stage, and optimizes the stage-specific decision tree in a non-greedy way. We conduct simulation studies to evaluate the finite sample performance of the proposed method and apply it to a real data application for optimal warfarin dose finding.

翻译：动态治疗方案（DTR）在精准医疗中扮演关键角色，其目标是在多阶段治疗中为患者指定个性化治疗方案，并优化长期临床结局。然而，现有DTR研究多聚焦于离散型治疗场景，而较少涉及连续型治疗选项。此外，常规黑箱机器学习方法与普通树学习算法分别存在可解释性不足与全局最优性欠缺的问题。本文提出一种针对剂量搜索的非贪心全局优化方法——全局最优剂量树基学习算法（GoDoTree），该方法将反事实结局均值的稳健估计与可解释的非贪心决策树相结合，用于估计多阶段设定下的全局最优动态剂量治疗策略。GoDoTree-Learning在每一阶段递归地利用双重稳健估计器估计反事实结局均值如何随连续治疗剂量变化，并以非贪心方式优化该阶段的决策树。我们通过模拟研究评估所提方法在有限样本下的表现，并将其应用于华法林最优剂量发现的真实数据案例。