Estimating individualized treatment rules (ITRs) is fundamental to precision medicine, where the goal is to tailor treatment decisions to individual patient characteristics. While numerous methods have been developed for ITR estimation, there is limited research on model updating that accounts for shifted treatment-covariate relationships in the ITR setting. In real-world practice, models trained on source data must be updated for new (target) datasets that exhibit shifts in treatment effects. To address this challenge, we propose a Reluctant Transfer Learning (RTL) framework that enables efficient model adaptation by selectively transferring essential model components (e.g., regression coefficients) from source to target data, without requiring access to individual-level source data. Leveraging the principle of reluctant modeling, the RTL approach incorporates model adjustments only when they improve performance on the target dataset, thereby controlling complexity and enhancing generalizability. Our method supports multi-armed treatment settings, performs variable selection for interpretability, and provides theoretical guarantees for the value convergence. Through simulation studies and an application to a real data example from the Best Apnea Interventions for Research (BestAIR) trial, we demonstrate that RTL outperforms existing alternatives. The proposed framework offers an efficient, practically feasible approach to adaptive treatment decision-making under evolving treatment effect conditions.

翻译：估计个体化治疗规则（ITRs）是精准医学的基础，其目标是根据患者个体特征定制治疗决策。尽管已有多种方法用于ITR估计，但在ITR情境下考虑治疗-协变量关系偏移的模型更新研究仍较为有限。在实际应用中，基于源数据训练的模型必须针对表现出治疗效果偏移的新（目标）数据集进行更新。为应对这一挑战，我们提出了一种勉强迁移学习（RTL）框架，该框架通过有选择地将关键模型组件（如回归系数）从源数据迁移至目标数据，实现高效的模型适应，且无需访问个体层面的源数据。借助勉强建模原理，RTL方法仅在模型调整能提升目标数据集性能时纳入调整，从而控制复杂度并增强泛化能力。我们的方法支持多臂治疗设置，执行变量选择以提高可解释性，并为价值收敛提供理论保证。通过模拟研究及应用于最佳呼吸暂停干预研究（BestAIR）试验的真实数据案例，我们证明RTL优于现有替代方法。所提出的框架为在治疗效果条件动态变化下的自适应治疗决策提供了一种高效且切实可行的途径。