标题： 一种用于估计分位数最优治疗方案的顺序分类学习 显示英文标题

标题： A sequential classification learning for estimating quantile optimal treatment regimes

摘要： 分位数最优治疗方案（OTRs）旨在分配能够最大化患者结果指定分位数的治疗。 与针对平均结果的治疗方案相比，当选择较低分位数时，分位数OTRs能提供更公平的方案，因为它专注于改善那些 otherwise 会经历相对较差结果的个体的结果。 在本文中，我们提出了一种估计分位数OTRs的新方法，通过将问题重新表述为顺序分类任务。 这种重新表述使我们能够利用强大的机器学习技术来提高计算效率并处理复杂的决策边界。 我们还研究了当结果为离散时分位数OTRs的估计，这是一个在文献中受到有限关注的设置。 一个关键挑战是，现有方法直接扩展到离散结果时往往会导致不一致和无效的问题。 为克服这一问题，我们引入了一种平滑技术，将离散结果映射到连续替代变量，从而实现一致且有效的估计。 我们提供了理论保证来支持我们的方法，并通过全面的模拟研究和真实数据分析展示了其优越性能。 显示英文摘要

摘要： Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients' outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it focuses on improving outcomes for individuals who would otherwise experience relatively poor results. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a sequential classification task. This reformulation enables us to leverage the powerful machine learning technique to enhance computational efficiency and handle complex decision boundaries. We also investigate the estimation of quantile OTRs when outcomes are discrete, a setting that has received limited attention in the literature. A key challenge is that direct extensions of existing methods to discrete outcomes often lead to inconsistency and ineffectiveness issues. To overcome this, we introduce a smoothing technique that maps discrete outcomes to continuous surrogates, enabling consistent and effective estimation. We provide theoretical guarantees to support our methodology, and demonstrate its superior performance through comprehensive simulation studies and real-data analysis.