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Abstract:
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Targeted therapies based on patients' baseline characteristics such as 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 dynamic adaptation 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 is necessary to estimate the subgroup effects adaptively based on the response outcomes and biomarker profiles from all the treated subjects at the interim analysis. To address this problem, we propose an adaptive subgroup-identification enrichment design, ASID, which can simultaneously search for predictive biomarkers, estimate the subgroups with differential treatment effects, and modify the study entry criteria at the interim analysis. We compare the ASID with an alternative adaptive enrichment design based on linear regression in a motivating Alzheimer's disease clinical trial, and demonstrate via simulation the superior performance of the ASID.
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