Podcast Episode
63: ICECAP: The Adaptive Design
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Scott Berry, Ph.D.
President & Senior Statistical Scientist
In this episode of "In the Interim…", Dr. Scott Berry is joined by Dr. Will Meurer, professor of Emergency Medicine and Neurology at the University of Michigan, for an in-depth analysis of the ICECAP trial’s adaptive Bayesian design.
In this episode of "In the Interim…", Dr. Scott Berry is joined by Dr. Will Meurer, professor of Emergency Medicine and Neurology at the University of Michigan, for an in-depth discussion of the ICECAP trial’s adaptive Bayesian design. The discussion breaks down the scientific rationale for hypothermia after cardiac arrest, critiques legacy studies, and explores the justification for including both shockable and non-shockable rhythm types. The episode provides a detailed account of ICECAP’s methodological strategies: a weighted mRS primary endpoint, Bayesian adaptive trial structure, response-adaptive randomization (governed by strict allocation guardrails), a unique Bayesian model for duration-response, and futility rules. The trial’s development is described in the context of the ADAPT-IT initiative, an FDA/NIH partnership, and the operational leadership of the MUSC Data Coordinating Center. Results are pending publication which will be highlighted in a future episode of “In the interim…”.
Key Highlights
Rationale for exploring duration of hypothermia after cardiac arrest with review of prior evidence.
Enrollment of shockable and non-shockable populations to address clinical uncertainty.
Primary endpoint: weighted mRS, independently developed for ICECAP.
Bayesian adaptive design with response-adaptive randomization, interim analyses, and allocation guardrails.
Management of missing data with multiple imputation from 30-day outcomes.
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63: ICECAP: The Adaptive Design
In this episode of "In the Interim…", Dr. Scott Berry is joined by Dr. Will Meurer, professor of Emergency Medicine and Neurology at the University of Michigan, for an in-depth analysis of the ICECAP trial’s adaptive Bayesian design.
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