Dynamic treatment regimes (DTR) are sequential decision rules corresponding to several stages of intervention. Each rule maps patients' covariates to optional treatments. The optimal dynamic treatment regime is the one that maximizes the mean outcome of interest if followed by the overall population. Motivated by a clinical study on advanced colorectal cancer with traditional Chinese medicine, we propose a censored C-learning (CC-learning) method to estimate the dynamic treatment regime with multiple treatments using survival data. To address the challenges of multiple stages with right censoring, we modify the backward recursion algorithm in order to adapt to the flexible number and timing of treatments. For handling the problem of multiple treatments, we propose a framework from the classification perspective by transferring the problem of optimization with multiple treatment comparisons into an example-dependent cost-sensitive classification problem. With classification and regression tree (CART) as the classifier, the CC-learning method can produce an estimated optimal DTR with good interpretability. We theoretically prove the optimality of our method and numerically evaluate its finite sample performances through simulation. With the proposed method, we identify the interpretable tree treatment regimes at each stage for the advanced colorectal cancer treatment data from Xiyuan Hospital.

翻译：动态治疗策略（DTR）是应对多阶段干预的序贯决策规则，每条规则将患者协变量映射到可选治疗方案。最优动态治疗策略是指在整体人群中实施时能最大化目标结果均值的策略。受一项关于晚期结直肠癌中医治疗的临床研究启发，我们提出一种截尾C学习（CC-learning）方法，利用生存数据估计含多种治疗方案的动态治疗策略。为应对多阶段右删失数据的挑战，我们修改了反向递归算法以适应灵活的治疗次数与时间节点。针对多治疗方案问题，我们提出基于分类视角的框架，将多治疗比较的优化问题转化为依赖样本的代价敏感分类问题。采用分类回归树（CART）作为分类器时，CC-learning方法生成的估计最优DTR具有良好的可解释性。我们从理论上证明了该方法的最优性，并通过模拟实验评估了其有限样本性能。应用所提方法，我们为西苑医院收治的晚期结直肠癌治疗数据识别了各阶段的可解释性树状治疗方案。