In this episode of "In the Interim...", Dr. Scott Berry investigates the practical meaning of fairness by connecting a controversial World Cup soccer ruling to foundational questions in clinical trial statistics. Scott scrutinizes FIFA’s unusual reversal of a red card suspension for US striker Folarin Balogun, referencing reports of US presidential influence, and draws explicit parallels between the enforcement of rules in international sport and the necessity for rigorously defined procedures in science. He references how systems thrive, or fail, on clear, consistently applied standards. Using Sherlock Holmes’ “Silver Blaze” and Abraham Wald’s WWII aircraft analysis, Scott revisits core statistical ideas about inference and missing data, survivorship bias, and the difference between prespecified versus post-hoc analyses. This episode affirms that adaptive and Bayesian approaches, when built on sound pre-specification and methodological discipline, represent scientific progress, offering a measured perspective on how standards and expectations of fairness continue to evolve.

Key Highlights:

• FIFA’s red card reversal, reports of external influence, and ramifications for procedural legitimacy
• Analogies from soccer, golf, baseball, and wrestling on the societal role of rules and enforcement
• Classic statistics lessons on missing data, inference, and survivorship bias
• Discussion of post-hoc versus prespecified analysis and its implications in trial integrity
• Defense of adaptive and Bayesian methodology as scientifically valid through pre-specification and covariate adjustment
• Reflection on the ongoing evolution of fairness and rigor in sport and science

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On the latest episode of "In the Interim...", Dr. Scott Berry and Dr. Kert Viele deliver a focused, technical analysis of statistical bias when stopping trials early. This episode clarifies the definition of bias, detailed within the context of interim analyses, emphasizing the empirical consequences of different stopping rules. The discussion addresses common misconceptions around interpretation as well as including the mathematical rationale for averaging across all trial outcomes, and the error of restricting bias estimates to only successful (early-stopped) trials. The hosts present a detailed critique of Bassler et al. (JAMA 2010), highlighting methodological flaws and misinterpretations of comparisons between truncated and non-truncated studies. Simulation is positioned as the primary tool for quantifying bias, with contextual examples illustrating the manageable magnitude of bias. Regulatory expectations are summarized, referencing formal FDA and ICH guidance on adaptive design bias assessment. The DAWN trial is cited as a real-world example where early stopping accelerated patient benefit.

Key Highlights

• Definition and quantification of bias in early-stopped clinical trials
• Mathematical examples demonstrating bias magnitude in fixed and adaptive group sequential designs
• Detailed critique of the methodology and conclusions in Bassler et al. (JAMA 2010)
• Discussion correcting common misunderstandings in bias estimation and selective reporting
• Simulation as a decisive tool for precise bias estimation
• Regulatory context including FDA guidance and ICH E20 draft guidance
• Reference to DAWN trial as evidence of practical benefits of early stopping

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On the latest episode of "In the Interim…", Dr. Scott Berry provides an empirical examination of two recent JAMA trials: TRACK (low-dose rivaroxaban in advanced kidney disease) and VICTORY (IV vitamin C in severe burn injury). The TRACK trial lacked any pre-specified futility criteria, with a DSMB-initiated stop based on conditional power calculations. Scott argues that conditional power, especially in this interim context, is a poor, misleading tool—contrasting it against a Bayesian predictive probability calculation that produced a much lower and more realistic estimate of success. In VICTORY, a pre-specified risk ratio threshold for futility was incorporated, with simulation confirming minimal effect on bias and statistical power. Scott underscores the practical and ethical importance of rigorously pre-specified, simulation-based futility rules and operationalizes the case for Bayesian predictive probability as a decision metric in interim monitoring. He reiterates that responsibility for defining futility belongs to trial designers, not left to ad hoc DSMB judgment, and calls for precise statistical planning in adaptive trial protocols.

Key Highlights

• TRACK: No pre-specified futility rule; DSMB stopped for futility using conditional power post hoc.
• Technical critique of conditional power as misguided at interim, supporting Bayesian predictive probability instead.
• VICTORY: Pre-specified futility threshold, with simulation confirming minimal operational bias and power reduction.
• Emphasizes pre-specified, simulation-based futility planning and predictive probability monitoring as standards for all trials.

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

In this episode of "In the Interim…", Dr. Scott Berry explores the origins of REMAP-CAP with Prof. Steve Webb, former chair of the REMAP-CAP International Trial Steering Committee. This episode examines how pandemic preparedness efforts after 2009 H1N1 shaped the design of an international, adaptive platform trial to be able to respond rapidly to new infectious threats. Steve and Scott explain the sequence of strategy meetings, the role of the PREPARE consortium in securing EU funding and subsequent federation across Australia and Canada. The discussion details REMAP-CAP’s technical foundations: a modular master protocol, domain architecture, Bayesian adaptive methods, and frequent interim analyses. When COVID-19 emerged, these core elements permitted immediate platform activation to combat the pandemic infection with assessment of treatments across multiple domains—including steroids, immune modulation, and anticoagulation—generating actionable evidence in weeks. The episode also addresses international data harmonization, multi-platform trial collaboration, and the capacity to adapt trial structure as infectious disease threats evolve.

Key Highlights

• Response to H1N1 and feckless pandemic trials
• International strategy meetings—origins of platform concept
• PREPARE consortium and cross-continental funding
• Modular master protocol, factorial allocation, and domain-specific appendices
• Bayesian triggers and response adaptive randomization
• Pivot to COVID-19 and rapid data generation
• Multi-platform international collaboration

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

In this episode of "In the Interim…", Dr. Scott Berry explores the challenge of protracted endpoint timelines in adaptive clinical trials and the statistical strategies used to increase the rate of actionable information gain. Drawing on detailed case studies from breast cancer (I-SPY 2), Alzheimer’s disease (BAN 2401), diabetes (AWARD-5/Trulicity), and cardiac arrest, Scott addresses the technical demands of longitudinal modeling and interim data imputation for accelerating learning. The discussion prioritizes a critical, empirical perspective of demonstrating how carefully constructed statistical models, simulation, and Bayesian methods can convert interim patient data into more robust estimates of delayed outcomes and support key design adaptations. The episode is a direct account of the methods, uncertainties, and real-world impact of fighting time in adaptive trials.

Key Highlights

• Analyzes how delayed primary endpoints challenge adaptive trial efficiency, and how adaptive trial designs use accumulating in-trial data to inform adaptive allocation, arm graduation, and early trial conclusions.
• Dissects the use of longitudinal models in I-SPY 2, in which interim MRI measurements at one and three months are mapped to predicted six-month pathologic complete response, through an ordinal stratified, pre-specified modeling approach—illustrating both the strengths and limits of interim forecasting.
• Reviews the BAN 2401 adaptive Alzheimer’s trial, where early cognitive assessments were modeled to forecast 12-month outcomes enabling response adaptive randomization and sample size adaptation based on projections from interim data.
• Details the AWARD-5 seamless trial for dulaglutide (Trulicity), where strategic enrollment pacing, predictive modeling of early HbA1c and weight loss, and a utility function across four endpoints supported both dose selection and seamless transition to phase 3 without requiring full cohort maturation.
• Summarizes recent cardiac arrest trial (ICECAP), using 30-day ordinal scales and multiple imputation to predict 90-day outcomes and improve interim decision-making.
• Unpacks the importance of prior-data-driven modeling, simulation, and strict robustness checks in the construction of all predictive models used for interim adaptation.

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

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/

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/