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Abstract:
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Targeted therapies based on patients' biomarkers have been growing interests for many diseases. Depending on the expression of specific biomarkers or their combinations, different patient subgroups could respond differently to the same treatment. An ideal design, especially at the proof of concept stage, should search for such subgroups and make adaptation to enrich the population been studied as the trial goes on. When no prior knowledge is available on whether the treatment works on the all-comer population or only works on the subgroup defined by one biomarker or several biomarkers, it's necessary to enroll all-comers at beginning and then estimate the treatment effect on the entire population as well as search for the subgroup with stronger treatment effects based on the accumulated response outcomes at the interim analysis. We propose a Bayesian Adaptive Subgroup-Identification enrichment Design, ASID, which can simultaneously search for predictive biomarkers, identify the subgroups with differential treatment effects, and modify the study entry criteria to enrich the population after a differential subgroup has been identified.
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