To achieve the goal of providing the best possible care to each individual under their care, physicians need to customize treatments for individuals with the same health state, especially when treating diseases that can progress further and require additional treatments, such as cancer. Making decisions at multiple stages as a disease progresses can be formalized as a dynamic treatment regime (DTR). Most of the existing optimization approaches for estimating dynamic treatment regimes including the popular method of Q-learning were developed in a frequentist context. Recently, a general Bayesian machine learning framework that facilitates using Bayesian regression modeling to optimize DTRs has been proposed. In this article, we adapt this approach to censored outcomes using Bayesian additive regression trees (BART) for each stage under the accelerated failure time modeling framework, along with simulation studies and a real data example that compare the proposed approach with Q-learning. We also develop an R wrapper function that utilizes a standard BART survival model to optimize DTRs for censored outcomes. The wrapper function can easily be extended to accommodate any type of Bayesian machine learning model.

为了实现为每个接受治疗的人提供尽可能最佳护理的目标，医生需要为具有相同健康状况的个人定制治疗方案，特别是在治疗可能进一步进展并需要额外治疗的疾病（例如癌症）时。随着疾病进展，在多个阶段做出决策可以形式化为动态治疗方案（DTR）。大多数用于估计动态治疗方案的现有优化方法（包括流行的 Q 学习方法）都是在频率论背景下开发的。最近，提出了一种通用贝叶斯机器学习框架，该框架有助于使用贝叶斯回归模型来优化 DTR。在本文中，我们在加速故障时间建模框架下的每个阶段使用贝叶斯加性回归树 (BART) 来调整这种方法以适应审查结果，并进行模拟研究和将所提出的方法与 Q 学习进行比较的真实数据示例。我们还开发了一个 R 包装函数，它利用标准 BART 生存模型来优化 DTR 以实现审查结果。包装函数可以轻松扩展以适应任何类型的贝叶斯机器学习模型。