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Dynamic treatment regimes (DTR) are sequential decision rules correspondingto several stages of intervention. Each rule maps patients' covariates tooptional treatments. The optimal dynamic treatment regime is the one thatmaximizes the mean outcome of interest if followed by the overall population.Motivated by a clinical study on advanced colorectal cancer with traditionalChinese medicine, we propose a censored C-learning (CC-learning) method toestimate the dynamic treatment regime with multiple treatments using survivaldata. To address the challenges of multiple stages with right censoring, wemodify the backward recursion algorithm in order to adapt to the flexiblenumber and timing of treatments. For handling the problem of multipletreatments, we propose a framework from the classification perspective bytransferring the problem of optimization with multiple treatment comparisonsinto an example-dependent cost-sensitive classification problem. Withclassification and regression tree (CART) as the classifier, the CC-learningmethod can produce an estimated optimal DTR with good interpretability. Wetheoretically prove the optimality of our method and numerically evaluate itsfinite sample performances through simulation. With the proposed method, weidentify the interpretable tree treatment regimes at each stage for theadvanced colorectal cancer treatment data from Xiyuan Hospital.

On Estimation of Optimal Dynamic Treatment Regimes with Multiple Treatments for Survival Data-With Application to Colorectal Cancer Study