Optimal individualized treatment rule for combination treatments under budget constraints
预算约束下组合治疗的最优个体化治疗规则AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以个体化治疗规则、治疗效应及组合治疗的价值优化为核心，直接对应因果决策与治疗政策学习。
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预算约束下组合治疗的最优个体化治疗规则
- 研究问题
- 面对可组合的多种治疗，如何根据个体特征制定治疗规则，并在资源预算有限时兼顾治疗交互作用与总体治疗价值？
- 方法与思路
- 作者提出非参数双编码器模型，以参数共享表示复杂治疗效应和交互效应，再通过多选择背包问题落实预算约束。
- 材料陈述的贡献
- 摘要称给出有无预算约束时的价值损失界及关于治疗数量的改进收敛速度，并在模拟和患者来源异种移植数据中展示优势。
- 阅读关注点
- 应核对治疗价值的定义与识别依据、双编码器的共享结构、预算优化的求解方式，以及损失界和比较优势的适用条件。
- 阅读边界
- 仅据所给Crossref摘要；治疗分配机制、识别假设、预算设定和具体数值未说明，摘要中的优势不能视为普遍优越性。
原文摘要
Abstract The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt 1 of 2 or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this paper, we propose a novel double encoder model (DEM) to estimate the ITR for combination treatments. The proposed double encoder model is a nonparametric model which not only flexibly incorporates complex treatment effects and interaction effects among treatments but also improves estimation efficiency via the parameter-sharing feature. In addition, we tailor the estimated ITR to budget constraints through a multi-choice knapsack formulation, which enhances our proposed method under restricted-resource scenarios. In theory, we provide the value reduction bound with or without budget constraints, and an improved convergence rate with respect to the number of treatments under the DEM. Our simulation studies show that the proposed method outperforms the existing ITR estimation in various settings. We also demonstrate the superior performance of the proposed method in patient-derived xenograft data that recommends optimal combination treatments to shrink the tumour size of the colorectal cancer.