Estimating the probability that a treatment outperforms a control for an individual patient, called the Individual Probability of Treatment Benefit (IPTB), offers a clinically intuitive alternative to population-average metrics. However, existing methods for IPTB estimation are largely confined to binary treatment settings, despite the prevalence of dose-varying interventions in clinical practice. We propose a general framework for IPTB estimation with ordinal outcomes under discrete dose assignments, called Dose-AIPTB (Dose Attention-based IPTB). Our approach recasts the problem as binary classification over the unobserved sign of the individual treatment effect, constructing pseudo-labels from covariate-similar pairwise comparisons and aggregating them via attention mechanisms or Nadaraya-Watson kernel regression. This formulation naturally accommodates multiple discrete dose levels, extending beyond the binary treatment paradigm. Through numerical experiments on real-world and synthetic data under covariate shift, varying sample sizes, and heterogeneous outcomes, we demonstrate that attention-based aggregation consistently outperforms kernel alternatives. The framework provides a foundation for personalized dose selection grounded in individual-level benefit probabilities. Codes implementing the model are publicly available at https://github.com/NTAILab/AIPTBDose.

翻译：评估某个治疗方案对个体患者的疗效优于对照方案的概率，即个体治疗获益概率（IPB），为群体平均指标提供了一种临床直觉更优的替代方案。然而，尽管临床实践中剂量变化干预普遍存在，现有IPB估计方法主要局限于二值治疗场景。我们提出了一种面向离散剂量分配下有序结局的IPB估计通用框架，称为Dose-AIPTB（基于注意力机制的剂量化IPB）。该方法将个体治疗效应不可观测符号的估计重构为二分类问题，通过协变量相似配对比较构建伪标签，并利用注意力机制或纳达拉雅-沃森核回归进行聚合。该公式自然兼容多个离散剂量水平，突破二值治疗范式限制。在协变量偏移、样本量变化和异质性结局下的真实与合成数据数值实验中，基于注意力的聚合方法始终优于核方法替代方案。该框架为基于个体层面获益概率的个性化剂量选择奠定了基础。实现该模型的代码已公开于https://github.com/NTAILab/AIPTBDose。