Heterogeneous treatment effect estimation is critical in oncology, particularly in multi-arm trials with overlapping therapeutic components and long-term survivors. These shared mechanisms pose a central challenge to identifying causal effects in precision medicine. We propose a novel covariate-dependent nonparametric Bayesian multi-treatment cure survival model that jointly accounts for common structures among treatments and cure fractions. Through latent link functions, our model leverages sharing among treatments through a flexible modeling approach, enabling individualized survival inference. We adopt a Bayesian route for inference and implement an efficient MCMC algorithm for approximating the posterior. Simulation studies demonstrate the method's robustness and superiority in various specification scenarios. Finally, application to the AALL0434 trial reveals clinically meaningful differences in survival across methotrexate-based regimens and their associations with different covariates, underscoring its practical utility for learning treatment effects in real-world pediatric oncology data.

翻译：异质性治疗效果估计在肿瘤学中至关重要，尤其是在具有重叠治疗成分和长期幸存者的多臂试验中。这些共享机制对精准医学中因果效应的识别构成了核心挑战。我们提出了一种新颖的协变量依赖非参数贝叶斯多治疗治愈生存模型，该模型联合考虑了治疗间的共同结构和治愈比例。通过潜在链接函数，我们的模型采用灵活的建模方法利用治疗间的共享机制，从而实现个体化生存推断。我们采用贝叶斯路径进行推断，并实现了一种高效的MCMC算法来近似后验分布。模拟研究证明了该方法在各种设定场景下的鲁棒性和优越性。最后，在AALL0434试验中的应用揭示了基于甲氨蝶呤方案间生存率的临床意义差异及其与不同协变量的关联，突显了该方法在真实世界儿科肿瘤学数据中学习治疗效果的实用价值。