通过降维优化治疗方案，提升个体化医疗决策准确性。
Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies
- 基于结果加权学习，用降维方法捕捉治疗效果差异的低维子空间。
- 在重症脓毒症数据中，准确识别出适合做超声心动图的患者群体。
- 兼顾混杂因素调整，适用于真实世界观察性研究，结果更可信。
个体化治疗方案（ITRs）旨在根据患者特征分配治疗以改善临床结果。然而，现有方法常受限于高维协变量，导致精度不足、可解释性差且难以落地。本文提出一种新的充分降维方法，直接关注潜在结果间的差异，识别出能捕捉治疗效果异质性的低维协变量子空间。该降维表示使基于结果加权学习的最优ITR估计更精准。为适应观察性数据，方法引入基于核函数的协变量平衡机制，允许治疗分配依赖完整协变量集，避免了传统假设——即用于建模治疗异质性的子空间也足以调整混杂偏倚。我们证明，在较弱正则条件下，所提方法具有普遍一致性，其风险收敛至贝叶斯风险。模拟与重症监护室脓毒症患者数据实证分析验证了其有限样本性能，成功确定了应接受经胸超声心动图的患者群体。
原文摘要 · Abstract (English)
Individualized treatment regimes (ITRs) aim to improve clinical outcomes by assigning treatment based on patient-specific characteristics. However, existing methods often struggle with high-dimensional covariates, limiting accuracy, interpretability, and real-world applicability. We propose a novel sufficient dimension reduction approach that directly targets the contrast between potential outcomes and identifies a low-dimensional subspace of the covariates capturing treatment effect heterogeneity. This reduced representation enables more accurate estimation of optimal ITRs through outcome-weighted learning. To accommodate observational data, our method incorporates kernel-based covariate balancing, allowing treatment assignment to depend on the full covariate set and avoiding the restrictive assumption that the subspace sufficient for modeling heterogeneous treatment effects is also sufficient for confounding adjustment. We show that the proposed method achieves universal consistency, i.e., its risk converges to the Bayes risk, under mild regularity conditions. We demonstrate its finite sample performance through simulations and an analysis of intensive care unit sepsis patient data to determine who should receive transthoracic echocardiography.
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