  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.
