Successive Classification Learning for Estimating Quantile Optimal Treatment Regimes

• 成果类型: Article; Early Access
• 署名作者: Xia, Junwen; Zhang, Jingxiao; Kong, Dehan
• 署名单位: Renmin University of China; Renmin University of China; University of Toronto
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2664228
• 发表日期: 2026-06-20
• 关键词: Discrete outcomes; Dynamic Treatment Regimes; fairness; personalized medicine; Smoothing technique; estimating individualized treatment; selection

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 welower quantile is selected, as it improves outcomes for vulnerable patients. In this article, 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. An implementation of our method in R is available at . for this article are available online, including a standardized description of the materials available for reproducing the work.