通过最优树进行高效子组分析的全局参数融合
Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion
AI总结：
本文提出了一种融合最优因果树方法，利用混合整数优化提升子组分析的准确性与统计效率，并在模拟和实际数据中验证了其有效性。
摘要：
识别和对差异治疗效应（在临床研究中通常称为子组分析）进行统计推断是精准医疗的核心。子组分析使从业者能够确定哪些人群对治疗特别有益或具有保护作用，从而推进靶向干预。基于树的递归划分方法由于其可解释性而被广泛用于子组分析。然而，这些方法存在显著局限性，包括由贪心启发式算法引起的次优划分和局部估计划分导致的过拟合，尤其是在样本量有限的情况下。为了解决这些局限性，我们提出了一种融合最优因果树方法，利用混合整数优化（MIO）来促进精确的子组识别。我们的方法确保了全局最优的划分，并引入了一个参数融合约束，以促进相关子组之间的信息共享。这种设计显著提高了子组发现的准确性，并增强了统计效率。我们通过严格建立样本外风险界限并将其与经典树方法的界限进行比较，提供了理论保证。在经验上，我们的方法在模拟中始终优于流行的基线方法。最后，我们通过健康与老龄化大脑研究健康差异（HABS-HD）数据集的案例研究展示了其实际用途，其中我们的方法产生了具有临床意义的见解。
英文摘要：
Identifying and making statistical inferences on differential treatment effects (commonly known as subgroup analysis in clinical research) is central to precision health. Subgroup analysis allows practitioners to pinpoint populations for whom a treatment is especially beneficial or protective, thereby advancing targeted interventions. Tree based recursive partitioning methods are widely used for subgroup analysis due to their interpretability. Nevertheless, these approaches encounter significant limitations, including suboptimal partitions induced by greedy heuristics and overfitting from locally estimated splits, especially under limited sample sizes. To address these limitations, we propose a fused optimal causal tree method that leverages mixed integer optimization (MIO) to facilitate precise subgroup identification. Our approach ensures globally optimal partitions and introduces a parameter fusion constraint to facilitate information sharing across related subgroups. This design substantially improves subgroup discovery accuracy and enhances statistical efficiency. We provide theoretical guarantees by rigorously establishing out of sample risk bounds and comparing them with those of classical tree based methods. Empirically, our method consistently outperforms popular baselines in simulations. Finally, we demonstrate its practical utility through a case study on the Health and Aging Brain Study Health Disparities (HABS-HD) dataset, where our approach yields clinically meaningful insights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
