Simultaneous inference for treatment regimes

A viable dynamic treatment regime refers to decisions regarding how different treatments and dose levels are tailored through time to match with the patient’s health status. In the therapy for cancer or diseases that require multiple stages of treatments, the effect of preceding treatment (such as growing back of solid tumors or regime-related toxicity) critically influences the selection of treatment in the following stage. So far, analyses of dynamic regimes mainly focus on marginal mean models under the assumption of sequential randomization in a clinical trial. Inference conclusions regarding multiple regimes are normally conservative due to different combinations in the formation of treatment regimes. In this article, we propose a simultaneous confidence interval method to identify treatment regimes that are significantly different from the bulk of the treatment combinations. The new method is applied to analyze the dynamic treatment regimes (DTRs) prostate cancer trial which includes treatment combinations of four chemotherapies at multiple stages. The new method detects a discernible effect of the regime taxane–estramustine–carboplatin (TEC) followed by cyclophosphamide, vincristine, and dexamethasone (CVD), when it is compared with other four regimes starting with CVD.