An individualized treatment rule (ITR) tailors treatments to a patient's specific characteristics. However, randomized controlled trials (RCTs) are often underpowered to detect the treatment effect heterogeneity needed for reliable ITR estimation. To address this limitation, there is growing interest in leveraging information from multiple studies to improve statistical power and support individualized decision-making. A key challenge in this context is that available RCTs may not evaluate the same set of treatments. In this paper, we propose an integrative learning framework that synthesizes evidence across multiple RCTs that share a common comparator but differ in their alternative treatment arms. Our method integrates information through a regularized weighted misclassification risk function and adaptively determines the contribution of each study to the ITRs of the others. We rigorously study the excess risk of the resulting estimator. Simulation studies demonstrate that the proposed approaches improve the estimation of both value and benefit functions. We illustrate the utility of our methodology using data from two landmark studies of major depressive disorder: the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care study and the International Study to Predict Optimized Treatment in Depression study, both of which include a selective serotonin reuptake inhibitor as a common treatment arm. We find that the separate learning method outperforms one-size-fits-all methods, and our integrative methods further improve performance.

翻译：个体化治疗规则（ITR）根据患者的具体特征量身定制治疗方案。然而，随机对照试验（RCT）通常难以检测到可靠的ITR估计所需的治疗效应异质性。为解决这一局限性，研究者日益关注利用多研究信息来提高统计功效并支持个体化决策。在此背景下，主要挑战在于现有RCT可能未评估相同治疗集合。本文提出一种整合学习框架，该框架综合共享共同对照药但替代治疗臂不同的多个RCT证据。我们的方法通过正则化加权误分类风险函数整合信息，并自适应地确定每项研究对其他研究所建ITR的贡献。我们严格分析了所得估计量的超额风险。模拟研究表明，所提方法改进价值函数与获益函数的估计。我们利用两项重度抑郁症里程碑研究的数据展示方法实用性：临床护理中抗抑郁反应调节因子与生物标志物确立研究，以及抑郁症优化治疗预测国际研究——两者均以选择性5-羟色胺再摄取抑制剂作为共同治疗臂。研究发现，独立学习方法优于"一刀切"方法，而整合方法进一步提升了性能。