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 functions 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 (EMBARC) study and the International Study to Predict Optimized Treatment in Depression (iSPOT-D) 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为其量身定制治疗方案。然而，随机对照试验(RCT)通常统计功效不足，难以检测出可靠估计ITR所需的处理效应异质性。为解决这一局限，利用多源研究信息提升统计功效并支持个体化决策逐渐受到关注。在此背景下，一个关键挑战在于现有RCT可能评估的治疗方案集合不尽相同。本文提出一种整合性学习框架，能够综合来自多个共享同一比较组但备选治疗组存在差异的RCT的证据。该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，所提方法能够同时改进价值函数和获益函数的估计效果。我们利用两项重度抑郁症里程碑式研究的数据验证了方法的实用性：临床护理中抗抑郁反应调节因子与生物标志物确立研究(EMBARC)和抑郁症优化治疗预测国际研究(iSPOT-D)，两项研究均包含选择性血清素再摄取抑制剂作为共同治疗组。研究发现，分离式学习方法优于"一刀切"方法，而我们提出的整合方法进一步提升了性能。