ICECAP: The Adaptive Design

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 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. For more, visit us at https://www.berryconsultants.com/ [https://www.berryconsultants.com/]

Multi-Platform RCT

In this episode of "In the Interim…", Dr. Scott Berry details the design, execution, and results of the multi-platform randomized clinical trial (mpRCT) pioneered during the COVID-19 pandemic. He describes how REMAP-CAP, ATTACC, and ACTIV-4a—each developed independently—pooled data prospectively for joint analysis to address therapeutic anticoagulation in hospitalized COVID-19 patients. Scott outlines the operational rigor required to harmonize endpoints, establish monthly adaptive analyses, and stratify patients by disease severity and D-dimer level. He examines the unified Bayesian hierarchical modeling approach, dynamic borrowing across strata, and the process for simultaneous DSMB reviews coordinated across all platforms. The mpRCT framework enabled real-time, evidence-based adaptations and rigorous distinction of treatment effect by patient subgroup. Results were incorporated into clinical guidelines because prospectively specified analysis revealed benefit for moderate patients and futility or harm for severe patients—findings that would have been missed by standard post hoc pooling. Key Highlights * Integration of REMAP-CAP, ATTACC, and ACTIV-4a under a prospectively unified analysis plan. * Primary endpoint and stratified patient subgroups defined in advance. * Monthly adaptive analyses using a shared Bayesian hierarchical model. * Simultaneous oversight by joint statistical and DSMB committees. * Superiority of therapeutic anticoagulation in moderate, non-critically ill groups; futility and possible harm in severe patients. * mpRCT model established a framework for future global multi-platform trials. For more, visit us at https://www.berryconsultants.com/ [https://www.berryconsultants.com/]

Sports and Clinical Trials: The 1927 Yankees, 15 Tarzans, and Modern Athletes

In this episode of "In the Interim…", Dr. Scott Berry examines the analytical challenges of comparing performance across eras in both sports and clinical research. Drawing from statistically robust family debates and published research, Scott details how overlapping competitors—such as athletes who played with both Babe Ruth, played with the next generation, who played with …  all the way to playing with Aaron Judge—enable the estimation of temporal effects and allow for objective comparisons between generations. He translates this approach directly into platform clinical trials, demonstrating how overlapping trial arms or shared control groups make it possible to quantify and adjust for time trends. Scott distinguishes between observable, model-based comparisons and subjective judgments, rigorously addressing limitations such as interactions between treatments and era, and emphasizing the foundational importance of empirical overlap over speculative claims. Key Highlights * Deconstruction of time-machine thought experiments: analyzing how teams like the 1927 Yankees or athletes such as Johnny Weissmuller and Jesse Owens compare to present-day counterparts using statistical benchmarks. * Technical explanation of connecting eras empirically through players or trial arms who span multiple time periods, thereby supporting quantitative estimation of temporal shifts. * Detailed account of linear and hierarchical modeling strategies, with covariate adjustment for player age, period effects, and evolving population composition across baseball, hockey, and golf data. * Translation of these statistical constructs to adaptive and platform clinical trials, exemplified by I-SPY 2, where overlapping treatment and control arms permit rigorous assessment of evolving treatment effects over a trial’s lifespan. * Critical discussion of the rare but important possibility of treatment-by-era interactions, and the necessity of data-driven assessment rather than assumption. * Consideration of how these methods inform not just debates about athletic greatness and Hall of Fame inclusion, but also robust interpretation of treatment effects in longitudinal clinical studies. For more, visit us at https://www.berryconsultants.com/ [https://www.berryconsultants.com/]

AI @ Berry

In the 60th episode of “In the Interim…”, Dr. Scott Berry, Dr. Nick Berry, and Dr. Joe Marion discuss how Berry Consultants uses AI in clinical trial design and software development. The conversation addresses current applications, limitations, implications for productivity, and the ongoing need for human expertise in clinical trial design. The team examines both promising use cases and the risks associated with security, compliance, and AI-generated statistical work. Key Highlights * AI is used to develop user interfaces and code modules, notably expediting tasks like R Shiny app development and software prototyping. * Statistical coding for complex modeling and simulation—such as numerical integration and predictive probability calculations—remains unreliable when delegated to AI and still requires direct oversight and manual review. * Attention to security and confidentiality is central; Berry prohibits the use of client-sensitive or patient data within AI tools. * Generative AI assists with drafting and editing documents, but the output tends to be non-specific, generic, and sometimes imprecise, requiring expert editorial input before use. * While embracing AI to improve efficiency, the discussion is critical of current AI hype, especially around black-box modeling and pushes back against the perception that current AI can replace domain-specific statistical design or strategic judgment. For more, visit us at https://www.berryconsultants.com/ [https://www.berryconsultants.com/]

Drug Development and Sports: The 10-Run Rule and Futility

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Nick Berry investigate how futility in clinical trials and stopping rules in sports illuminate very similar decision problems, albeit with very different consequences. Drawing from baseball’s 10-run rule, tournament cuts in golf, the discussion confronts traditional and Bayesian strategies for interim decisions. The episode explains why simulation, not historical trial review, provides the empirical backbone for futility boundaries in clinical trials, and details the mechanics and consequences of aggressive stopping criteria. Using the Biogen aducanumab Alzheimer’s trials, the conversation exposes how a futility rule based on 20% predictive probability halted trials even when meaningful probability of success remained. Scott and Nick address the influence of ethical considerations, cost, regulatory priorities, and statistical rigor, and contrast Bayesian predictive probability’s strengths over conditional power. Key Highlights * Dissects sports futility rules (10-run rule, golf cuts, Bill James heuristic) and their application to clinical trial design * Argues for prospective simulation to define adaptive futility thresholds * Explains how Bayesian predictive probability provides a more robust framework than conditional probability for interim adaptive decisions * Details how aggressive futility criteria may prematurely stop trials and risk missing beneficial treatments, as in the aducanumab case * Explores the intersection of ethics, patient safety, operational efficiency, regulatory standards, and trial cost