Abstract Number:
3848
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Junzhe Shao (1), Waverly Wei (1), Jingshen Wang (1)
Institutions:
(1) UC Berkeley, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Adaptive enrichment design has become increasingly important in modern clinical trials and drug discovery by enhancing resource efficiency and potentially accelerates scientific discoveries. Compared with traditional randomized controlled trials, adaptive enrichment designs allow the flexible revision of enrollment criteria based on interim data, focusing future enrollment on subpopulations that show positive responses. However, traditional adaptive enrichment designs potentially compromise statistical power and faces challenging statistical inference due to the dependency of enrollment on previous selections. In this work, we propose a unified adaptive enrichment framework offering two primary benefits: First, our design not only allows the revision of enrollment criteria but also the adjustment of treatment allocation strategy. Second, our framework integrates design and inference seamlessly, ensuring valid statistical analysis throughout the process. Through theoretical investigations and simulation studies, we demonstrate the effectiveness of the framework in maintaining type I error rates and enhancing statistical power.
Keywords:
Randomized trial|Adaptive design|Subgroup selection| | |
Sponsors:
Biometrics Section
Tracks:
Personalized/Precision Medicine
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