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Targeted therapies based on biomarker profiling are becoming a mainstreamdirection of cancer research and treatment. Depending on the expression ofspecific prognostic biomarkers, targeted therapies assign different cancerdrugs to subgroups of patients even if they are diagnosed with the same type ofcancer by traditional means, such as tumor location. For example, Herceptin isonly indicated for the subgroup of patients with HER2+ breast cancer, but notother types of breast cancer. However, subgroups like HER2+ breast cancer witheffective targeted therapies are rare and most cancer drugs are still beingapplied to large patient populations that include many patients who might notrespond or benefit. Also, the response to targeted agents in human is usuallyunpredictable. To address these issues, we propose SUBA, subgroup-basedadaptive designs that simultaneously search for prognostic subgroups andallocate patients adaptively to the best subgroup-specific treatmentsthroughout the course of the trial. The main features of SUBA include thecontinuous reclassification of patient subgroups based on a random partitionmodel and the adaptive allocation of patients to the best treatment arm basedon posterior predictive probabilities. We compare the SUBA design with threealternative designs including equal randomization, outcome-adaptiverandomization and a design based on a probit regression. In simulation studieswe find that SUBA compares favorably against the alternatives.

Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials