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Activity Number:
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642
- Data-Driven Modeling in Medical and Health Policy Decision Making
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Type:
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Invited
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Date/Time:
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Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #322342
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Title:
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Multi-Arm Bayesian Designs for Clinical Trials
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Author(s):
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Lorenzo Trippa*
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Companies:
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Dana-Farber Cancer Institute, Harvard
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Keywords:
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clinical trials ;
Biomarker ;
Designs ;
Bayesian
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Abstract:
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Biomarker-based clinical trials provide efficiencies during therapeutic development and form the foundation for precision medicine. These trials must generate information on both experimental therapeutics and putative predictive biomarkers in the context of varying pre-trial information. We generated an efficient, flexible design that accommodates various pre-trial levels of evidence that support the predictive capacity of a biomarker while making pre-trial design choices explicit. We generated a randomization procedure that explicitly incorporates pre-trial estimates of the predictive capacity of biomarkers. To compare the utility of this "Bayesian Basket (BB)" design relative to a balanced randomized, biomarker agnostic (BA) design and to a traditional "basket" (TB) design that includes only biomarker positive patients, we iteratively simulated hypothetical multi-arm clinical trials under various scenarios.
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Authors who are presenting talks have a * after their name.
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