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 a real data application that recommends optimal combination treatments for Type-2 diabetes patients.
翻译:个性化治疗规则 (ITR) 基于个体特征推荐最优治疗,已经吸引了许多领域的关注,例如精准医学、个性化教育和个性化营销。现有的 ITR 估计方法主要采用两种或多种治疗方法中的一种。然而,多种治疗的组合可能在各种领域中更加强大。在本文中,我们提出了一种新的双编码器模型 (DEM) 来估计个性化组合治疗下的个性化治疗规则。所提出的双编码器模型是一个非参数模型,不仅灵活地融合了复杂治疗效应和治疗之间的交互作用效应,还通过参数共享功能提高了估计效率。此外,我们通过多项选择背包问题,将估计的 ITR 量身定制为预算限制,从而增强了我们在受限资源情况下提出的方法。在理论上,我们提供了存在或不存在预算限制的价值减少界限,并在 DEM 下相对于治疗次数提供了改进的收敛速度。我们的模拟研究表明,在各种设置中,所提出的方法优于现有的 ITR 估计方法。我们还展示了所提出的方法在为二型糖尿病患者推荐最优组合治疗方面的卓越性能。