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 improves outcomes for vulnerable patients. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a successive classification task, solvable via training a sequence of classifiers, each successive classifier built on the output of its predecessors. 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.

翻译：分位数最优治疗策略（OTRs）旨在通过分配治疗方案来最大化患者结局的特定分位数。与以平均结局为目标的治疗策略相比，当选择较低分位数时，分位数OTRs能够提供更公平的策略，因为它改善了弱势患者的结局。本文提出了一种估计分位数OTRs的新方法，通过将该问题重构为一个连续分类任务来解决，该任务可通过训练一系列分类器来完成，其中每个后续分类器都基于其前驱分类器的输出构建。这种重构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。我们还研究了结局为离散值时分位数OTRs的估计，这一设置在现有文献中关注有限。一个关键挑战在于，将现有方法直接扩展到离散结局常会导致不一致性和无效性问题。为克服此问题，我们引入了一种平滑技术，将离散结局映射到连续代理变量，从而实现一致且有效的估计。我们提供了理论保证以支持所提方法，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。