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 one of two 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 individualized treatment rule 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 PDX data that recommends optimal combination treatments to shrink the tumor size of the colorectal cancer.

翻译：个体化治疗规则（ITR）根据个体特征推荐最优治疗方案，已在精准医学、个性化教育和个性化营销等领域引起广泛关注。现有ITR估计方法主要从两种或多种治疗方案中择一推荐，但多方案组合治疗在多个领域可能更具优势。本文提出一种新颖的双编码器模型（Double Encoder Model, DEM），用于估计组合治疗的个体化治疗规则。该双编码器模型是一种非参数模型，不仅能灵活融合复杂的治疗效应及治疗间的交互效应，还通过参数共享特性提升估计效率。此外，我们通过多选择背包问题公式将估计的ITR适配至预算约束，增强了所提方法在资源受限场景下的适用性。理论上，我们给出了有/无预算约束下的价值降低界，以及DEM框架下随治疗方案数量增加而优化的收敛速率。模拟研究表明，所提方法在多种设置下均优于现有ITR估计方法。我们还在PDX数据中验证了该方法在推荐最优组合治疗方案以缩小结直肠癌肿瘤体积方面的优越性能。