Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments.
Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments.
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学习最优的群体结构的个体化治疗规则与多种治疗。
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Liu Y
中科院分区：
文献类型：
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作者：
Ma H;Zeng D;Liu Y
数据驱动的个性化决策问题近年来受到了广泛的关注。特别是，决策者的目标是确定最佳的个性化治疗规则（ITR），以便最大限度地提高对异质患者特定特征的预期指定结果的平均值。许多现有方法处理二元或中等数量的治疗组，并且可能不考虑潜在的治疗效果结构。然而，当治疗组的数量变大时，这些方法的有效性可能恶化。在这篇文章中，我们提出了组结果加权学习（GROWL）估计潜在的结构在治疗空间和最佳组结构的ITR通过一个单一的优化。特别是，估计组结构的ITR，我们利用加强角度为基础的多类别支持向量机（RAMSVM）学习基于组的决策规则下的加权角度为基础的多类分类框架。建立了Fisher相合性、超额风险界和价值函数的收敛速度，为GROWL提供了理论保证。大量的仿真研究和真实的数据分析的实验结果表明，GROWL享有更好的性能比其他几个现有的方法。
Data driven individualized decision making problems have received a lot of attentions in recent years. In particular, decision makers aim to determine the optimal Individualized Treatment Rule (ITR) so that the expected specified outcome averaging over heterogeneous patient-specific characteristics is maximized. Many existing methods deal with binary or a moderate number of treatment arms and may not take potential treatment effect structure into account. However, the effectiveness of these methods may deteriorate when the number of treatment arms becomes large. In this article, we propose GRoup Outcome Weighted Learning (GROWL) to estimate the latent structure in the treatment space and the optimal group-structured ITRs through a single optimization. In particular, for estimating group-structured ITRs, we utilize the Reinforced Angle based Multicategory Support Vector Machines (RAMSVM) to learn group-based decision rules under the weighted angle based multi-class classification framework. Fisher consistency, the excess risk bound, and the convergence rate of the value function are established to provide a theoretical guarantee for GROWL. Extensive empirical results in simulation studies and real data analysis demonstrate that GROWL enjoys better performance than several other existing methods.