Explorez tous les épisodes du podcast In the Interim...

In this episode of "In the Interim…", Dr. Scott Berry recounts a presentation he gave to the Speak Foundation, a patient organization focused on limb-girdle muscular dystrophy and makes the case for master protocols directly to the stakeholder trials exist to serve. Opening with the deliberately absurd premise of a sports league that constructs a four-billion-dollar stadium for every single game and demolishes it at the final whistle, Scott reframes the conventional one-off trial as a two-year construction project dismantled the moment the contest ends. The episode distinguishes platform trials and basket trials as standing "arenas," explains the modular role of the master protocol described in Woodcock and LaVange's 2017 New England Journal of Medicine paper, and draws on worked examples including I-SPY 2, the HEALEY ALS Platform Trial, GBM AGILE, Precision Promise, and the ROAR basket trial. Scott takes each stakeholder in turn from the treatment arm sponsor, the site, the regulator, and the patient. Scott lays out how three-to-one randomization lets an arm fund 133 patients while reading out inferences with more than 200, how 667 patients in a single platform can do the work of 1,000 across five separate trials, and why far fewer patients are assigned to placebo. He closes on the genuine bottleneck: the first arena is the heavy lift, and in rare disease it is frequently patients themselves who make it happen.

Key Highlights

• The one-game stadium analogy of two years to build a trial, one contest, then demolition.
• Platform trials and basket trials as standing arenas for multiple arms and multiple subtypes.
• Master protocols as modular documents, with arms plugging in and out as appendices.
• Worked examples from I-SPY 2, HEALEY ALS, GBM AGILE, Precision Promise, and the ROAR basket trial.
• A stakeholder-by-stakeholder view: arm sponsor, site, regulator, and patient.
• 667 patients in one platform versus 1,000 across five separate trials, with far fewer on placebo.
• Bayesian borrowing across 39 limb-girdle subtypes, and the rare-disease shots never otherwise taken.

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

In this episode of "In the Interim…", Dr. Scott Berry talks with Dr. Alun Bedding, a professional certified coach and statistician who spent decades in pharmaceutical statistics, including as global head of statistical methods, collaboration, and outreach at Roche, before founding his own coaching and consulting practice. The conversation traces Alun's path from statistician to coach and unpacks what coaching is: a forward-facing partnership built on exploring a client's own thinking, not on advice, mentoring, or therapy. Alun and Scott draw a direct line between good coaching and good statistical consulting as both begin by questioning the stated goal rather than accepting the first request at face value, whether that request is "I want to be a better manager" or "I need a power calculation." The episode covers why coaching must be uncomfortable to produce real change, why self-coaching runs into the limits of blind spots and self-bias and where AI coaching tools genuinely help and where they fall short.

Key Highlights

• Coaching is a partnership exploring a client's own thinking, not advice or mentoring.
• The parallel between coaching and statistical consulting: determining the real goal behind a request.
• Why effective coaching must feel uncomfortable to produce genuine progress.
• The limits of self-coaching, and why blind spots require an outside challenger.
• How sports and performance coaching shifted from instruction to inquiry.
• Where AI coaching helps and where human presence and challenge remain irreplaceable.
• The difference between coaching and therapy: forward-facing versus backward-facing.

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

In this episode of “In the Interim…,” Dr. Scott Berry talks with Dr. Roger Lewis, Dr. Anna McGlothlin, and Dr. Nick Berry — all co-authors on the CHIPS trial results recently published in JAMA (Spinella et al., August 2026) — about the design, implementation, and results. The conversation covers why room-temperature platelets, which can be stored only five to seven days, leave rural hospitals, low-volume centers, and military and disaster settings without a reliable supply, and how a Bayesian adaptive design was used to find the maximum safe cold-storage duration rather than testing a single fixed duration. Roger, Anna, and Nick walk through the monotonic dose-response model that governed escalation, an unplanned mid-trial complication when the FDA independently authorized fourteen-day cold storage, and how the Data Safety Monitoring Board reviewed results within days of each interim. The trial ultimately demonstrated non-inferiority of cold-stored platelets out to twenty-one days with a Bayesian probability greater than 99.9%, offering a path to expanding platelet access in settings where it was previously very challenging.

Key Highlights

• Cold-stored platelets tested as a potential answer to platelet shortages in rural, low-volume, and military/disaster settings.
• Bayesian adaptive design used to find the maximum safe cold-storage duration, not just test a single fixed duration.
• A monotonic dose-response model constrained escalation to a pre-specified, safety-first ladder across four interim analyses.
• Mid-trial complication: an independent FDA decision allowing 14-day cold storage, absorbed into the design without unblinding.
• Non-inferiority demonstrated out to 21 days of cold storage, with a Bayesian probability greater than 99.9%.
• Interim data turned around by the unblinded implementation team in five business days, versus the six weeks often assumed for adaptive trials.
• Implications for platelet access in disaster, military, and low-volume hospital settings, and for how shelf-life-dependent products are tested going forward.

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

In this episode of "In the Interim…", Dr. Scott Berry examines three recent health-research headlines through a statistician's lens: an Adventist Health Study-2 analysis claiming eggs reduce Alzheimer's risk, an Emory University trial on high-dose vitamin D and cognition (Zhao et al.), and a British Medical Journal study led by Dr. Daniel Danishvar (Harvard, Boston University) reporting that 25 to 97 percent of deceased NFL players showed evidence of chronic traumatic encephalopathy (CTE). After flagging multiplicity and small-sample issues in the first two studies, Scott spends most of the episode on the CTE study's central flaw: because CTE can only be diagnosed after death, the published prevalence is calculated from a sample of deceased players rather than the full population of NFL players — a distinction he illustrates using a thought experiment on sudden infant death syndrome (SIDS) and a breakdown of the study's own age-stratified death data. He traces the resulting bias, a form of differential mortality, to a single caveat buried deep in the study's limitations section.

Key Highlights

• Adventist Health Study-2's "27% fewer Alzheimer's diagnoses in egg-eaters" finding, and why it's likely multiplicity-driven and observational, not causal.
• The Emory University vitamin D study (Zhao et al.): a 13% MoCA improvement drawn from roughly eight patients, presented as a headline finding.
• The British Medical Journal NFL CTE study (Danishvar et al., Harvard / Boston University): a headline prevalence of "25% to 97%" of NFL players.
• Why 215 of 235 donated brains (97.7%) is a hugely biased numerator — CTE is only diagnosed posthumously, and families of symptomatic players are most likely to donate.
• The corrected denominator: 878 NFL players who died between 2016 and 2021, yielding roughly 24.5% — still biased, because CTE itself accelerates death.
• A SIDS thought experiment showing how building a "population" from those who have already died systematically overstates prevalence through a form of differential mortality bias.
• The key limitation, buried three-quarters of the way through the paper's limitations section, quietly mentions the selection bias behind the headline number.

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

In this episode of "In the Interim...", Dr. Scott Berry dissects prevailing concepts of “clinically meaningful difference” in clinical trials focusing on progressive diseases. Through detailed examples from pancreatic cancer (Ben Sasse, Revolution Medicines), emphysema (Elevair), Alzheimer’s disease (lecanemab), and IVF, Scott challenges the adequacy of the population-mean of a continuous outcome in reflecting true patient benefit. The episode discusses inconsistent usage and interpretation of acronyms such as MCID, CSD, and Target Product Profile (TPP). Dr. Berry advises trialists to resist interpreting the mean difference using patient-level minimal effects, and adopt responder analyses and cumulative probability approaches to enhance patient-level relevance. Guidance is offered for analyzing the effect of time-saved instead of a mean differences in a clinical endpoint at a single time for progressive diseases – measuring “sweet time.”

Key Highlights

• Focus on added time not the change from baseline as the most meaningful outcome for a progressive disease.
• In-depth evaluation of MCID, CSD, TPP, and risk of misinterpretation.
• Critique of trying to interpret mean-based endpoints for clinical meaningfulness such as six-minute walk distance and CDR sum of boxes.
• FDA advisory panel guidance on MCID for IVF live birth endpoints and dichotomous versus continuous endpoints.
• Advocacy for responder analyses and cumulative probability of achieving thresholds in reporting the clinical effect of a treatment.

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

In this episode of "In the Interim…", Dr. Don Berry provides a detailed account of co-chairing the 1997 NIH consensus development conference on mammography for women in their 40s. His conversation with Dr. Scott Berry covers the statistical and clinical complexities of breast cancer screening, addressing lead time and length bias, trial design limitations. Don discusses the panel’s finding, based on randomized trials and meta-analysis, that the average benefit of screening women in their 40s is modest (an estimated 1.4-day average life extension and 18% hazard reduction). The panel recommended individualized decision-making rather than universal screening. The episode follows the reaction: heated debate with radiologists, scrutiny from journalists and policymakers, Senate testimony, and personal threats. Don explains how these findings became distilled into soundbites, evidenced by coverage in The New York Times, Chicago Tribune, and a reference in James Patterson’s Murder Games. The conversation addresses overdiagnosis, false positives, and the consequence some called the “Berry effect”—an observed drop in mammogram rates after the panel’s recommendations.

Key Highlights

• Don Berry’s NIH consensus conference leadership and approach to breast cancer screening recommendations.
• Statistical bias and trial limitations inherent in mammography evidence.
• Meta-analysis findings: 18% hazard reduction; 1.4-day average life extension.
• Policy guidance supporting individualized patient decision-making over universal screening.
• Strong backlash from advocacy, media, radiology, and government—including Senate hearings and personal threats.
• Enduring debates, public misunderstandings, and continued citation of these events in scientific and popular sources.

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

In this episode of "In the Interim…", Dr. Scott Berry speaks with Dr. Srinivas Murthy, Dr. Thomas Hills, and Dr. Lindsay Berry about the REMAP-CAP trial results on oseltamivir in critically ill influenza patients. The trial used a Bayesian covariate-adjusted platform design and found oseltamivir was not effective at reducing 90-day mortality with a “98% and 99% probability of harm in 90-day mortality” compared to control. Covariate adjustment addressed baseline and site variation. Subgroup analyses showed greater harm in patients with higher illness severity. Sensitivity analyses using alternative neutral, optimistic, and pessimistic priors produced important scientific exploration of the results. No evidence was found for benefit over control in any subgroup.

The discussion highlights the first randomized, controlled evidence in this patient group, contrasting prior observational studies and clinical guidelines. A mechanism of harm remains unclear. REMAP-CAP is continuing enrollment in moderate severity and pediatric cohorts to further examine population-specific effects. The episode also addresses the broader challenges of trial design and interpretation in acute care research, the limitations of nonrandomized evidence, and the importance of ongoing Bayesian analyses and transparent reporting.

Key Highlights

• Pre-print is available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7172531
• REMAP-CAP platform trial, Bayesian logistic regression, covariate adjustment
• Oseltamivir arms: statistical trigger for inferiority, “98 and 99% probability of harm”
• Greater harm in sicker subgroups, consistent results across sensitivity analyses
• Ongoing arms: moderate severity and pediatric cohorts, mechanistic questions unresolved
• Context: limitations of previous historical data studies, clinical practice impact, future research directions

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

In this episode of "In the Interim…", Dr. Scott Berry leads a comprehensive discussion of the ICECAP trial results with four Principal Investigators: Dr. Will Meurer (Professor, Emergency Medicine and Neurology, University of Michigan; consultant to Berry Consultants), Dr. Robert Silbergleit (Professor, Emergency Medicine, University of Michigan Medical School), Dr. Romer Geocadin (Professor, Neurology, Neurosurgery, and Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine), and Dr. Sharon Yeatts (Professor of Biostatistics, Public Health Sciences, Medical University of South Carolina). The panel dissects the ICECAP trial’s multi-arm Bayesian adaptive design, response-adaptive randomization, and population-level approach to cooling duration after out-of-hospital cardiac arrest. Emphasis is placed on methodological transparency, direct operational experience, absence of evidence for incremental benefit beyond six hours of cooling, and future direction for neurocritical care trials.

Key Highlights

• Detailed review of adaptive design methodology, Bayesian interim analyses, and stopping criteria for futility based on posterior probabilities
• Analysis of flat duration-response curve: no clinical benefit seen for extended hypothermia, trial triggered to stop per prespecified rule
• Cohort discussion: representation of U.S. epidemiology, inclusion of heterogeneous etiologies (notably respiratory and overdose) and bystander CPR rates
• Operational challenges: running frequent interim analyses, maintaining trial integrity during COVID-19, site-level differences, statistical reporting timelines
• Panel consensus on the need for continued equipoise in temperature management, caution against misinterpretation, and priority for precision subgroups in future research
• Directions: implementation lessons, pediatric ICECAP, PRECISE-CAP phenotyping study, ongoing subgroup analyses

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

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Elizabeth Lorenzi systematically examine the analytic pitfalls in recent acute ischemic stroke trials, especially the implications of violating the proportional odds assumption on the modified Rankin Scale. The discussion draws on the DISCOUNT, INSTANT, ESCAPE-MeVo, and ORIENTAL-MeVo trials, spotlighting frequent reactive shifts to proportional odds violations. Scott and Liz detail how such approaches obscure clinically relevant heterogeneity and react by creating analysis methods that obscure the clinical relevance of a violation of proportional odds. The episode underscores the necessity for trial designs that explicitly address heterogeneity of treatment effect. Listeners gain an unvarnished critique of prevailing reporting practices and an actionable vision for future stroke trial designs.

Key Highlights

• DISCOUNT trial’s interim analysis and futility stopping.
• Issues with endpoint dichotomization after proportional odds violations.
• Comparison across multiple recent stroke trials with inconsistent endpoint definitions.
• Obscuring of proportional odds violations, which may be the most important result of the trial.
• Adaptive strategies in STEP platform.

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

In this episode of "In the Interim…", Dr. Scott Berry challenges the widely held belief that any interim look at trial data obligates an alpha adjustment. By constructing a two-by-two matrix: interim data (positive/negative) and adaptive action (increase/decrease sample size), Scott demonstrates that the need for statistical correction depends on precisely what actions are prespecified. He emphasizes that the need for adjustment depends on the action and the data. Technical scenarios examined include group sequential designs, “promising zone” sample size re-estimation (citing the formal results of Mehta and Pocock), and response adaptive randomization. Scott stresses that clear prespecification is required for Type I error control and regulatory compliance. He critiques common missteps, such as unnecessary allocation of alpha to futility boundaries when superiority is not planned, and reiterates that it is the adaptive action, and not mere data review, that determines the statistical impact of interim analyses.

Key Highlights

• Dissects alpha adjustment myths and their historical roots.
• Details two-by-two matrix: interim data direction and adaptive action.
• Explores group sequential, futility, promising zone, and response adaptive examples.
• Clarifies when Type I error is truly affected—action and data matter.
• Stresses prespecification’s role in trial validity and regulatory acceptance.
• Identifies pitfalls in common trial design practices.

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

In this episode of "In the Interim…," Dr. Scott Berry interviews Tim Berry, co-founder of Blend360, detailing a career that demonstrates the practical application of statistical and analytical methods within large-scale business environments. Tim outlines his quick shift from earning a master’s at the University of Minnesota to industry positions, starting at AT&T Bell Laboratories, where he built and tested retention models on millions of consumer records. He recounts his time at Rapp Collins, where analytics had limited organizational impact, before joining Merkle and transforming analytics into a key business component through growing a team from two to over eighty, contributing to hundreds of millions in revenue. At Blend360, Tim discusses acquiring Consultants To Go (C2G) to build new capabilities, focusing on hiring and developing young analytics talent through programs like All-Star. The conversation addresses the evolution from traditional statistics to analytics, the rise of AI and agentic AI for workflow automation and calls out media overstatement of AI-driven disruption. He concludes with pointed career advice to his nephew and other quantitative students: prioritize adaptability, industry experience, and continuous learning over chasing credentials.

Key Highlights:

• Graduate thesis using the Bradley-Terry model for baseball outcome prediction
• Mainframe-driven, large-scale retention modeling at AT&T
• Analytics as a peripheral function at Rapp Collins versus central driver at Merkle
• Rapid talent expansion and analytics leadership at Merkle
• Founding Blend360 and institutional talent development
• AI advancements, agentic AI for business, skepticism on AI hype
• Concrete advice for quantitative undergraduates

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

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

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

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

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

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 and Dr. Kert Viele examine response-adaptive randomization (RAR) in clinical trials, dissecting its statistical rationale, common criticisms, and implementation challenges. Drawing on extensive experience with trials such as BAN2401 (lecanemab), ICECAP, dulaglutide seamless Phase 2/3, I-SPY2, REMAP-CAP, PROSPECT, and the historical ECMO trial, they discuss the scientific advantages and disadvantages and ethical impact. RAR reallocates patient assignments during interim analyses to direct more patients to better-performing arms, but this can reduce power in two-arm trials, introduce complexity from temporal trends, and create operational complexity. The ECMO trial and "play-the-winner" approaches are discussed as cautionary examples emphasizing the need for thorough simulation before deployment. The hosts highlight RAR’s strengths for dose-finding, multi-arm, and some platform designs, but underscore its limitations in confirmatory two-arm settings. Operational demands, data reliability, simulation across scenarios, and resistance to overgeneralization are recurrent themes. The episode concludes by situating RAR within the broader context of adaptive platform trials and learning healthcare systems.

Key Highlights

• Definition and mechanics of RAR, with interim analysis guiding allocation updates
• Multi-arm adaptive and platform trial experiences (BAN2401, ICECAP, dulaglutide, I-SPY2, REMAP-CAP, PROSPECT)
• Critique of RAR in two-arm trials (power loss), temporal trends, unblinding, and overgeneralized literature
• ECMO/play-the-winner: risks of poorly simulated RAR
• Necessity for rigorous pre-trial simulation and robust data flows
• Contextualization of RAR’s role in both traditional and learning healthcare environments

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/

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/

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/

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

In this episode of "In the Interim…", host Dr. Scott Berry undertakes a detailed, methodical critique of ICH-E20 draft guidance language as applied to adaptive clinical trial design. Focusing on an innocuous but corruptible paragraph in Section 3.1, Scott scrutinizes the logic behind regulatory reluctance to appreciate multiple or complex adaptations in confirmatory trials. Drawing on extensive experience, he highlights how such restrictive interpretations do not reflect practical development realities, instead setting up “false choices” where alternative designs desired by regulators are infeasible. Through operational scenarios—including the SEPSIS-ACT trial, an enrichment design, and sample size re-estimation examples—Scott illustrates the empirical benefits of seamless and multi-adaptive trials for sponsors, patients, and regulators. Technical discussion addresses misconceptions about complexity and bias and stresses the value of presenting realistic alternatives when engaging with regulatory authorities. The episode ultimately encourages a more nuanced dialogue to advance efficient and scientifically robust clinical trials.

Key Highlights

• Discussion of ICH-E20 section 3.1 guidance and its operational impact on adaptive designs.
• Dissection of “false choice” dilemmas in regulatory interactions, referencing real adaptive trial submissions.
• Case-based examples: SEPSIS-ACT, enrichment, and sample size adaptation trials.
• Highlighting myths regarding bias and operational burden from multiple interim analyses.
• Emphasis on practical strategies for more effective regulatory communication about adaptive trials and realistic alternatives.

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

In this episode of "In the Interim…" Dr. Scott Berry is joined by Professors Victoria Cornelius, Danny McAuley, and Anthony Gordon, for a technical review of the PANTHER trial—an international, Phase 2 adaptive platform evaluating pharmacologic interventions for ARDS. The trial is open-label and does not employ blinding, as discussed in the episode. The primary endpoint is 28-day organ support-free days (death as -1, survivors 0–28 days), analyzed with a Bayesian proportional odds model. PANTHER uses  stratification by hyper- and hypoinflammatory subphenotypes, with fixed, equal randomization within each stratum. Analyses for treatments are separated by stratum, reflecting the potential of differential treatment effects. Quarterly interim analyses allow early stopping by stratum for efficacy or futility. Content includes explicit discussion of infrastructure: rapid device deployment, centralized data for trial and future biological discovery, and governance challenges in multinational collaboration. Funding is provided by NIHR (UK), US Department of Defense, CIHR (Canada), NHMRC and MRFF (Australia), HRB (Ireland), and additional support from Germany and Japan. PANTHER is positioned to streamline Phase 2 critical care drug testing and facilitate graduation to larger platforms such as REMAP-CAP, with potential to expedite pharmaceutical evaluation and accelerate ARDS therapeutic development.

Key Highlights

• Real-time phenotyping (Randox device) to stratify ARDS patients.
• Separate Bayesian analyses by phenotype stratum.
• Open-label, fixed randomization within stratum.
• 28-day organ support-free days as a composite endpoint.
• Quarterly interim analyses enable early dropping or graduation of arms by strata.
• Central data resource and biosample collection for future research.
• Operational, funding, and device logistics for global trial deployment.
• Transition of Phase 2 results to established Phase 3 platforms (e.g., REMAP-CAP).

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

In this episode of "In the Interim…", Dr. Scott Berry connects with Dr. Byron Gajewski, professor of biostatistics and data science at the University of Kansas Medical Center (KUMC), for a detailed discussion on the design, simulation, and operational realities of Bayesian adaptive clinical trials in academic environments. Gajewski discusses his academic background, training at Texas A&M, and progressive adoption of Bayesian principles based on direct experiential advantages in complex data settings. The conversation highlights KUMC’s Fixed and Adaptive Clinical Trial Simulator Working Group, which utilizes FACTS for faculty, staff, and student collaboration, enabling practical simulation, trial protocol development, and in-house applied statistical training. The PAIN-CONTRoLS Trial serves as a practical example of multi-arm Bayesian adaptive design, using response-adaptive randomization for comparative effectiveness in neuropathy research. The NIH-funded HOBIT trial is examined in detail: multi-arm structure, adaptive allocation among investigational arms, fixed control randomization, group-sequential interim analyses, and sliding dichotomy methodology for the Glasgow Outcome Scale Extended. The discussion stresses a shift to probabilistic, evidence-driven interpretation and reporting, shaping operational choices and academic culture for both investigators and trainees.

Key Highlights

• Gajewski describes how practical challenges in real-world problems catalyzed his transition to Bayesian modeling.
• KUMC’s working group integrates FACTS software in collaborative simulation and operational trial planning.
• The PAIN-CONTRoLS Trial: multi-arm Bayesian adaptive design, response-adaptive randomization, real-time analysis, and endpoint-driven allocation.
• HOBIT trial: Adaptive allocation, fixed control arm proportion, group-sequential interims, ordinal endpoint modeling.
• Emphasis on probabilistic, quantitative reporting over binary outcomes in trial analysis and interpretation.
• Cultural shift observed among academic collaborators and trainees embracing Bayesian adaptive strategies.

In this episode of "In the Interim...", Dr. Scott Berry hosts Dr. Stephen Senn, award-winning statistician and author, for a discussion on advanced challenges in adaptive and platform trial methodology. Senn draws on experience in academic, pharmaceutical, and regulatory settings to address the recent draft guidance on Bayesian statistics from the FDA and multiple controversies in clinical trial design.

Key Highlights

• Emphasizes understanding data origin and regression to the mean as essential for trial interpretation, above adherence to Bayesian or frequentist frameworks.
• Details methodological considerations for time adjustments and model complexity, highlighting that model specification and parameter handling are critical regardless of statistical school.
• Identifies the limitations of non-concurrent controls in platform trials, focusing on evolving background therapy, site participation, and protocol changes that reduce validity of historical or pooled control data.
• Analyzes blinding difficulties in trials with multiple treatments and administration modes, using “veiled” blinding as a case and noting the implications for placebo response comparability.
• Clarifies that operational efficiencies are the principal advantage of adaptive and platform trials, while purported statistical efficiencies can be exaggerated.
• Stresses the importance of presenting interim analyses transparently to DSMBs when using complex models for time or covariate adjustment, to ensure oversight and interpretation remain rigorous.

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

In this episode of "In the Interim…", Dr. Scott Berry is joined by statisticians Dr. Amy Crawford, Dr. Cora Allen-Savietta, and Dr. Jessica Overbey for a technical deep dive into hierarchical composite endpoints and the win ratio in clinical trial design. The group addresses clinical and statistical justifications for layered endpoint structures, demonstrates the mechanics of pairwise win ratio analysis, and explores operational and interpretive consequences in both conventional and adaptive trials. The panel scrutinizes analytic limitations, regulatory concerns, and emerging modeling strategies—all grounded in real-world trial examples.

Key Highlights

• Precise definition and use case for hierarchical composite endpoints in cardiovascular and related trials.
• Stepwise breakdown of win ratio mechanics, tie-handling, and the distinction between effect estimation (win ratio) and hypothesis testing (FS-test).
• Discussion of endpoint prevalence and dominance, risk of clinical interpretation being tied to lower-order outcomes, the role of patient exposure, and methods to parse component contributions.
• Overview of statistical power, role of simulation, and comparative advantages over other composite approaches.
• Identification of core limitations: interpretive complexity, opaque weighting, and mutable meaning of wins with maturing data.
• Review of predictive probability for adaptive interim analysis and modeling using ordinal regression.
• Opinions of US and European regulatory perspectives including support, reservations, and expectations for transparency with graphics and complementary analyses.

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

In this episode of "In the Interim…", Dr. Scott Berry interviews Professors Steven Tong and Josh Davis about the SNAP platform trial for Staphylococcus aureus bacteremia. The discussion covers SNAP’s rationale, large-scale adaptive design, methodology, and operational execution at approximately 150 hospitals in 13 countries. Key statistical questions, domain results, pediatric-adult analysis, and global implementation strategy are explored in depth. Listeners will find clear examples of how adaptive platform trials can efficiently address clinically relevant questions in infectious disease, while highlighting the nuances of trial design, statistical thresholds, and network collaboration.

Key Highlights

• High and unchanging mortality for Staphylococcus aureus bacteremia—over one million deaths annually.
• SNAP leverages silo-based structure (MSSA, MRSA, PSSA) and factorial domains for simultaneous, efficient investigation of treatments.
• Cefazolin shown non-inferior to flucloxacillin for MSSA with lower related acute kidney injury.
• In PSSA, penicillin demonstrated significantly less toxicity and favorable mortality signal over flucloxacillin; mortality difference did not meet the statistical superiority threshold.
• Futility reached in the adjunctive clindamycin domain for effect on 90-day mortality.
• Both adults and children enrolled, with pediatric results using statistical borrowing from adults in line with FDA Bayesian guidance.
• Ongoing platform expansion includes bacteriophage therapy, antiplatelet domains, and evaluation of diagnostic strategies.
• Statistical leadership: Dr. Anna McGlothlin (Berry Consultants), Dr. Julie Marsh (statistics lead).

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

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine Bayesian borrowing in Phase 3 clinical trials, focusing on statistical handling of prior information and real-world FDA interactions. The episode opens with an analogy, comparing prior probability in Bayesian analysis to interpreting a home pregnancy test, succinctly demonstrating the effect of prior knowledge on trial interpretation. The discussion addresses technical challenges—how borrowing inflates Type I errors and why this is addressed differently under Bayesian operating characteristics. Concrete examples include dynamic versus static borrowing approaches, and formal integration of prior evidence in regulatory submissions. Case studies center on the WATCHMAN device (PROTECT AF and PREVAIL trials) and REBYOTA, illustrating FDA engagement, relevant trial design tactics, and published outcomes. The episode also critiques common pitfalls such as selective data use and improper prior construction, emphasizing the FDA’s focus on comprehensive and unbiased historical sources.

Key Highlights

• Pregnancy test analogy used to clarify prior probability in trial interpretation.
• Bayesian borrowing’s effects on Type I error and statistical thresholds.
• Case studies: WATCHMAN device (PROTECT AF, PREVAIL) and REBYOTA approvals.
• Dynamic borrowing versus static borrowing strategies in regulatory settings.
• Risks of cherry-picking and importance of unbiased, relevant prior data.
• FDA guidance and review procedures for Bayesian trials.

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

In Episode 51 of "In the Interim…", Dr. Scott Berry interviews writer, producer, and performer Shaun Cassidy to examine the practical elements of storytelling that matter in scientific and statistical communication. Cassidy draws on his experience in television, music, and live performance—including his role as writer and Executive Producer of New Amsterdam—to present clear parallels between audience engagement in show business and in clinical research. The conversation prioritizes improving narrative precision, emotional resonance, and authenticity when conveying complex topics to varied audiences.

Key Highlights

• Cassidy demonstrates that audiences retain emotional impact over factual content, asserting that “people don’t remember what you say, but how you made them feel.”
• Emphasis on narrative specificity: personal, concrete details foster stronger audience connection than generalized statements, countering assumptions about broad relatability.
• Effective communication relies on reactive delivery—improvised response to audience cues—rather than rigid, memorized scripts; Cassidy notes this principle applies across disciplines.
• Role of authenticity and vulnerability: openly stating discomfort or introversion facilitates greater audience trust and personal connection, especially in technical or scientific fields.
• Anecdotes from Cassidy’s work in television, music, and teaching illustrate the central role of storytelling structure and audience feedback, with parallels drawn to professional scientific presentations.
• Alan Alda’s illustration of improv for scientists is discussed as an example of bridging technical expertise with adaptive communication skills.

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

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Lindsay Berry investigate the statistical foundations and clinical implications of analyzing ordinal endpoints, drawing on experience from major stroke and COVID-19 trials. Discussion centers on the Modified Rankin Scale, DAWN, MR CLEAN, and REMAP-CAP, demonstrating that methods such as proportional odds, dichotomization, and utility weighting all impose explicit or implicit clinical weights on the outcome categories. The episode presents direct mathematical derivations, exposes the equivalence between proportional odds models and value-weighted analysis, and uses real trial data to explore how statistical and clinical perspectives on endpoint weighting may diverge. Emphasis remains on transparency and the need for clinically relevant weight assignment in trial endpoints.

Key Highlights

• Structural overview and clinical significance of the Modified Rankin Scale scores.
• Illustration that proportional odds models and dichotomized analyses apply hidden, prevalence-driven or threshold-based weights.
• Utility weighting in DAWN, formulated from EQ-5D patient utilities and economic studies, with observed alignment.
• MR CLEAN investigators' critique of utility weighting; empirical data demonstrated relative consistency and challenged the claim that statistical approaches resolve variation across patients.
• REMAP-CAP platform trial: Organ Support Free Days endpoint analyzed with proportional odds imposed weights on the scale from death to free of organ support .
• Extension of these arguments to win ratio/rank-based approaches, with caution that all methods encode clinical assumptions.

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

In this episode of "In the Interim…", Dr. Scott Berry marks the podcast’s one-year anniversary, sharing listener metrics, watch data, and regional engagement. He then delivers a step-by-step analysis of the FDA meeting process, detailing the progression from initial sponsor meeting requests and question submission to briefing book preparation, feedback cycles, and in-person logistics for a Type C meeting at the White Oak facility. Drawing from more than 25 years of trial design and regulatory experience, Scott offers precise guidance on technical preparation, sponsor responsibilities, and common errors in sponsor-FDA dialog, emphasizing what works and what wastes time inside the one-hour meeting constraint. His practical approach focuses on clarity, respect for process, and actionable advice.

Key Highlights

• Slightly over 30,000 people tuned in during the first year across 45 episodes; about 10,000 via audio, 20,000 via video with a global worldwide reach.
• FDA meeting workflow: request, submit four to eight questions, draft briefing book, receive written feedback, strict one-hour in-person discussion controlled by sponsor.
• Advice on briefing book content, avoiding new materials at the meeting, even what not to bring through the White Oak facility.
• Sponsor pitfalls: disingenuous patient advocacy, asking impossible questions, taking adversarial stance in statistical discussion.

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

Dr. Nathan O’Hara (University of Maryland), Dr. Gerard Slobogean (UC Irvine), and Dr. Sheila Sprague (McMaster University) describe the launch and design of the Musculoskeletal Adaptive Platform Trial (MAPT)—the first major adaptive platform trial in orthopaedic surgery. The discussion covers MAPT’s master protocol structure, patient-centered endpoint framework, and operational strategies for multinational implementation. Focus areas include the FASTER-HIP domain’s use of Bayesian modeling with a hierarchical clinical endpoint and the standards established for adaptation, data coordination, and future scalability. Listeners gain insight into a trial infrastructure designed to lower barriers for evidence generation and facilitate ongoing evidence generation in musculoskeletal trauma care.

Key Highlights

• MAPT as a scalable, master protocol for orthopaedic intervention evaluation
• Hierarchical, patient-centered endpoint (survival, 4-level ambulation, days alive/out of hospital), analyzed with a Bayesian-modeled, non-parametric win ratio
• Domain-specific adaptation thresholds based on clinical differentiation
• Interim analyses after 100 patients, then every 50, informing early adaptation
• 40 sites across US, Canada, and Europe, centralized data management at McMaster
• A unified DSMB structure with capacity for domain-specific expertise as needed
• Tiered protocol access: open sharing, collaboration, direct integration
• Infrastructure enables rapid domain addition and multi-investigator participation

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

In this episode of "In the Interim…", Dr. Scott Berry speaks with Dr. Michael Harhay, Associate Professor at the University of Pennsylvania and Director of the Center for Clinical Trials Innovation. The conversation explores Dr. Harhay’s progression through neuroscience, philosophy, epidemiology, and statistics, examining how this academic path shapes his work in clinical trial methodology. They discuss the Center’s role in addressing unresolved methodological questions arising from pragmatic, health system-based trials, including challenges with cluster and factorial randomized designs. The episode focuses on statistical and conceptual issues in endpoint selection for critical care, such as the analysis of informatively truncated outcomes, composite endpoints including organ support-free days, and the application of the win ratio. The increasing use of Bayesian methods in trial design is addressed.

Key Highlights

• Dr. Harhay’s academic background and transition into clinical trial methodology at Penn.
• The mission of the Center for Clinical Trials Innovation to support methodologic research and training, particularly among statisticians participating in multi-center health system trials.
• Discussion of hospital-level and provider-level randomization strategies in cluster and factorial designs within health systems.
• Ongoing challenges in analysis of composite and informatively truncated endpoints, especially in critical care, exemplified by ventilator-free and organ support-free days.
• Evaluation of analytic strategies including survival average causal effect, composite endpoints, and the win ratio, with emphasis on the need for clinical rather than purely statistical weighting of outcomes.
• Consideration of the conceptual strengths of Bayesian methods and their integration into modern trial design and decision analysis.

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

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele deliver a quick reaction to the FDA’s draft guidance on Bayesian statistics for clinical trials of drugs and biologics. Their assessment addresses the structure, content, and impact of the document, emphasizing evidence-based requirements and guidance scope. The episode breaks down regulatory language, technical expectations, and workflow implications for clinical trial sponsors and statisticians.

Key Highlights

• Clear distinction between trials justified by type 1 error control and trials justified by agreement on Bayesian priors and decision rule.
• Explanation of how informative priors can be created based on external or historical data.
• Technical explanation of dynamic discounting/borrowing, especially in Bayesian hierarchical models for rare populations, pediatric-adult extrapolation, related disease subgroups, and platform and basket trials (e.g., ROAR).
• In-depth look at the necessity of sensitivity and robustness checks for different priors, and the FDA’s design prior and analysis prior terminology.
• FDA’s requirements for accepting external data sources: data provenance, patient-level comparability, recency, and appropriate covariate adjustments.
• Comparison with ICH E20 on adaptive designs, providing context for ongoing regulatory harmonization and possible influence on international regulatory directions.
• Direct warning against attempts to misuse Bayesian methodology as a substitute for scientific rigor; legitimate uses must meet FDA standards and not simply serve to lower evidentiary bars.

Resource:  FDA News Release:  https://www.fda.gov/news-events/press-announcements/fda-issues-guidance-modernizing-statistical-methods-clinical-trials

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

In this episode of "In the Interim…", Dr. Scott Berry is joined by Dr. Tanya Simuni, Arthur C. Nielsen Jr. Professor of Neurology and Director of the Parkinson’s Disease and Movement Disorders Center at Northwestern University, and Dr. Barbara Wendelberger, Senior Statistical Scientist at Berry Consultants. The conversation focuses on the Path to Prevention (P2P) platform trial—an international, multi-arm prevention study in Parkinson’s disease targeting participants defined by biological markers, specifically alpha-synuclein pathology, prior to clinical diagnosis. The discussion covers the PPMI cohort, trial operational and statistical structure, the rationale behind biomarker-driven inclusion, and the use of Bayesian platform trial design.

Key Highlights:

• Parkinson’s disease pathobiology and risk: genotype-phenotype variability, multi-system involvement, and the central roles of age, environment, and genetics.
• Michael J. Fox Foundation’s PPMI cohort: 4,000+ participants, prospective longitudinal biomarker and clinical data, high participant retention, enabling study of early Parkinson’s.
• P2P platform structure: multi-arm design, two-stage randomization with shared placebo group, integration of non-randomized PPMI cohort in Bayesian analysis for improved inference.
• Inclusion criteria: prodromal population biologically defined by CSF alpha-synuclein seed amplification and dopaminergic imaging (DAT-SPECT), highlighting regulatory nuances.
• Dual primary endpoints: biomarker (DAT-SPECT) and clinical (MDS-UPDRS Part III), 24-36 months follow-up.
• Commitment to public data sharing in line with the Michael J. Fox Foundation’s open science philosophy.

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

In this episode of “In the Interim…,” host Dr. Scott Berry examines the challenge of communicating complex statistical concepts to non-statistical audiences. Drawing from firsthand experiences in agriculture, professional golf, and clinical development, as well as examples involving historical and scientific figures, Scott reflects on why technical rigor alone often fails to influence. The discussion focuses on the consequences of mismatched language, the importance of empathy, and the utility of simulation when bridging the gap between analysis and stakeholder understanding.

Key Highlights

• Illustrated barriers to statistical communication using stories from farming, golf, and early career encounters.
• Examples involving John Glenn, Ada Lovelace, and Charles Babbage show how communication, not just science, determines impact.
• Insights from Alan Alda on empathy as a foundational tool for scientists presenting technical ideas.
• Clinical trial simulations revealed knowledge gaps—such as misunderstanding of power—when communicating with decision-makers.
• Emphasizes the necessity of translating analytic outputs into operational, financial, or clinical language for meaningful impact.

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

In this episode of "In the Interim…", host Dr. Scott Berry and frequent co-host Dr. Kert Viele, Senior Statistical Scientist at Berry Consultants, analyze the potential shift in FDA regulatory policy from requiring two independent trials to accepting a single trial as sufficient for “substantial evidence” in drug approvals. Reflecting on the statutory and regulatory definitions originating with the 1962 Federal Food, Drug, and Cosmetic Act and 21 CFR 314.126, they dissect current and emerging interpretations, referencing recent statements by Dr. Martin Makary and coverage described in a STAT article. The conversation focuses on the scientific and statistical foundations of the two-trial threshold, challenges with dichotomous results, and how pooled evidence might increase efficiency and rigor. They discuss statistical implications including alpha thresholds, sample size effects, program power, and the consequences for clinical labeling. The episode also introduces Bayesian approaches as a method for integrating totality of evidence. Attention is given to both population breadth and the possible risks of a narrowed evidentiary base under a single-trial standard.

Key Highlights

• Regulatory and historical context of “substantial evidence” since 1962 and current FDA directives.
• Industry practice: simultaneous Phase III trials, statistical power, and evidentiary replication.
• Criticism of binary, trial-level significance thresholds; merits of pooling or meta-analysis.
• Potential efficiency gains and tradeoffs with a more stringent alpha requirement for single trials.
• Strategic and operational effects on trial design, sample size, and label indications.
• Bayesian statistical approaches for full evidence integration, discussed as an analytical viewpoint.

In this episode of "In the Interim…", Dr. Jenny Devenport, Global Head of Methods, Collaboration, and Outreach at Roche, joins Dr. Scott Berry for a detailed discussion on career evolution, statistical culture, and communication in the pharmaceutical industry. Dr. Devenport describes her transition from psychology in New Mexico to statistical leadership in Basel, emphasizing the formative role of early academic mentors and her experience working across the US and Europe. She outlines her current functions in methods development, internal collaboration, and industry outreach, highlighting active engagement with academic and regulatory communities. The episode scrutinizes differences in workplace culture, such as the emphasis on debate and long-term collaboration in Europe, and differences in educational backgrounds among statisticians. The conversation covers practical barriers and slow adoption of Bayesian methods and the importance of communication in the acceptance of futility analyses in pharma, the importance of scale in problem-solving, and the emergence of AI as a tool for statisticians. Dr. Devenport provides pragmatic strategies for statisticians to improve their influence through tailored, audience-specific communication.

Key Highlights

• Dr. Devenport’s academic and geographic move from the US to Europe
• Responsibilities in methods development, collaboration, and outreach at Roche
• Contrasts in US and European pharmaceutical statistics cultures
• Measured perspective on AI’s effect on statisticians’ responsibilities
• Practical guidance for statisticians on communication and influence

In this episode of "In the Interim…", Dr. Scott Berry delivers a metaphoric critique of single-question trial infrastructure through the sports arena analogy, illustrating the cost, patient burden, and data inefficiency of conventional clinical trials. He provides a methodical comparison of traditional trial models and the platform trial approach, clarifying distinctions between platform, basket, and master protocol structures. Through examples from HEALEY ALS, I-SPY 2, PALM (Ebola), REMAP-CAP, RECOVERY, EPAD, GBM AGILE, and Precision Promise, Scott outlines the measurable efficiencies of platform trials: shared control arms, flexible arm addition and removal, reduced placebo exposure, accelerated timelines, and improved statistical inferences. The episode further examines platform trial performance during the COVID-19 pandemic, highlighting  trial adaptability, and the rapid generation of actionable evidence. Scott also addresses failure scenarios, focusing on EPAD Alzheimer’s as a cautionary case in platform sustainability, cost allocation, and initial funding barriers. Listeners will gain a perspective on the operational and statistical design choices governing today’s most innovative clinical studies.

Key Highlights

• Arena analogy applied to delineate clinical research inefficiency.
• Operational, statistical, and patient-focused efficiencies in platform versus single-question trials.
• Precision in terminology: platform, basket, and master protocol definitions.
• Effects of platform trials on speed and scientific rigor.
• Factors underlying both platform trial successes and failures.

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

In episode 40 of "In the Interim…", Dr. Scott Berry examines the statistical, operational, and behavioral challenges of using interim analyses as triggers for funding in adaptive and seamless Phase II/III clinical trials. The episode presents a typical hypothetical scenario for rare disease drug development, contrasting conventional two-stage development with a seamless design and highlighting efficiency gains in sample size, patient allocation, and trial duration. Scott details the construction of administrative (financial) interim analyses, underscoring their distinction from futility analyses and their role in funding decisions when complete funding is not secured upfront. He addresses FDA operational bias concerns, emphasizing blinding and limiting information sharing to protect trial integrity. Finally, the episode focuses on developing objective interim funding criteria—using Bayesian predictive probability and assurance—and on leveraging illustrative simulation outputs and sample datasets to bridge the “I’ll know it when I see it” divide between scientists and funders. Practical, empirical, and tailored to real funding barriers in clinical research.

Key Highlights

• Statistical structure and efficiency of seamless Phase II/III trial designs
• Administrative (financial) interim analysis setup as funding decision triggers, distinct from futility analyses
• FDA operational bias guidance and requirements for trial blinding
• Predictive probability and assurance as objective interim criteria
• Sample data and simulation outputs to facilitate stakeholder alignment

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

In this episode of "In the Interim...", Dr. Scott Berry interviews Dr. Kaspar Rufibach, Co-Head of Advanced Biostatistical Sciences at Merck. The conversation tracks Rufibach’s evolution from academic training in actuarial and mathematical statistics through cancer research collaborations, postdoctoral work, and academic consulting, leading to applied roles in Roche and Merck. Discussion centers on methodological rigor, pragmatic approaches to assurance and predictive probability, and real-world experience in drug development. Rufibach examines the organizational integration of quantitative disciplines at Merck—incorporating pharmacology, real-world data, statistics, programming, and data science—while remaining candid on the role and boundaries of AI in current pharmaceutical practice.

Key Highlights

• Statistical education in Switzerland, bridging theory and early applied cancer trial experience
• Move from academic consulting to a trial statistician role at Roche, emphasizing structured problem-solving in drug development
• Approach to predictive probability and assurance, balancing Bayesian and frequentist tools with strict emphasis on practicality
• Formation of professional special interest groups with EFSPI and PSI, stepping in to address unmet community needs rather than seeking formal leadership
• Perspective on Merck’s unified quantitative department, designed to remove silos and leverage interdisciplinary expertise
• Cautious view of AI as a complement to specific tasks, but not yet a replacement for nuanced clinical trial design or regulatory-facing strategies
• Current focus on expanding causal inference methods and multi-state modeling for improved trial efficiency and evidence synthesis

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

In this episode of "In the Interim…" guest host Cooper Berry moderates a detailed discussion on the evolution and practice of Bayesian methodology in clinical trials with fellow family members Dr. Don Berry, Dr. Scott Berry, Dr. Lindsay Berry, and Dr. Nick Berry. The panel outlines the foundational principles of Bayesian decision-making in medical research, ethical debates informed by historical reports like the Belmont Report, and the shift in regulatory acceptance. Computational developments such as Markov Chain Monte Carlo (MCMC) are examined for their role in enabling applied Bayesian models. Panelists give practical accounts of implementing adaptive and platform trials, including I-SPY 2 and REMAP-CAP, and analyze challenges faced during the COVID-19 pandemic. The implications of Bayesian statistics in artificial intelligence and contemporary clinical decision-making are explored, highlighting ongoing shifts in trial design and evidence synthesis. Each discussion is grounded in direct experience and technical rigor, providing insight into both the operational realities and future trajectory of Bayesian-driven methods in clinical research.

Key Highlights:

• Historical development of Bayesian clinical trial design and foundational influence from Leonard J. Savage to current methods
• Ethical tension in trial conduct, referencing the Belmont Report and equipoise
• Advances in computation and Markov Chain Monte Carlo (MCMC)
• Regulatory frameworks for Bayesian adaptive trials, including FDA guidance
• Implementation details from I-SPY 2 and REMAP-CAP platform trials
• Bayesian methodology in the context of artificial intelligence, precision medicine, and future data integration

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

In episode 37 of "In the Interim…", Dr. Jeff Saver, Director of the UCLA Comprehensive Stroke and Vascular Neurology Program, details his shift from behavioral neurology to clinical stroke research after early engagement with multicenter trials like TOAST. The discussion covers the biology of acute ischemic stroke, quantifying neuronal loss, and the scientific underpinnings of “time is brain.” Dr. Saver outlines the evolution of endovascular therapy, from early device challenges to current reperfusion success rates exceeding 85%. Key methodological issues in stroke trial analyses are presented, including debate over endpoint selection—dichotomous versus ordinal approaches and the limitations therein. Special focus is placed on the utility-weighted modified Rankin Scale, which assigns empirically derived, patient-centered health values to each disability state, providing a comprehensive measure that captures both benefit and harm. The episode explores regulatory hesitancy, differing analytic preferences within the field, and the design prospects for neuroprotectant interventions. Heterogeneity in patient outcomes and implications for public health and trial methodology are addressed. The episode provides an empirical account of clinical trial endpoint selection, interpretation, and future directions in cerebrovascular research.

Key Highlights

• Early career influences and pivotal trial participation.
• Pathophysiology and quantification of acute stroke injury.
• Endovascular device development and clinical impact.
• Comparative analysis of endpoint methods: dichotomous, ordinal, and utility-weighted approaches.
• Technical derivation and application of utility-weighted mRS.
• Ongoing regulatory and methodological debate.
• Heterogeneity in ischemic vulnerability and future trial directions.

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

In Episode 36 of "In the Interim…", Dr. Scott Berry and Dr. Don Berry analyze the Phase II trial of Lecanemab (BAN2401) in Alzheimer’s disease, focusing on the application of adaptive Bayesian methods following persistent failures in Alzheimer’s drug development. The conversation covers the specific design features of five active arms, response adaptive randomization, and a longitudinal Bayesian model driving interim decisions, as well as direct operational and statistical challenges encountered during the trial. The hosts address regulatory proceedings, critique from "experts" regarding adaptive methods on noisy cognitive endpoints, and the direct alignment of the trial’s Bayesian 18-month efficacy estimates with the subsequent Phase III results and regulatory approvals.

Key Highlights

• Alzheimer’s drug development context: Widespread Phase III failures prompted a retreat from conventional trial designs and a demand for greater rigor and adaptability.
• Lecanemab Phase II methodology: Five active arms, two dosing schedules, response adaptive randomization, and adaptive interim analyses at every 50 patients enabled real-time adjustment and efficient dose evaluation.
• Bayesian modeling and imputation: Use of a longitudinal model to address missing data, forecast 12- and 18-month outcomes, and inform both allocation and stopping criteria.
• Operational adaptations: The design accommodated unplanned safety restrictions, such as stratified randomization for APOE4-positive participants after ARIA signals.
• Expert skepticism: Addressed Paul Aisen’s concerns about adapting to noisy interim cognitive data, emphasizing safeguards against erroneous stopping or success.
• Regulatory outcome: The 18-month efficacy estimates from Bayesian modeling during Phase II matched Phase III findings; FDA granted accelerated approval based on amyloid reduction and later full approval after Phase III confirmation.

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

On this episode of “In the Interim…”, which is co-sponsored by the Journal of Statistics and Data Science Education, Dr. Scott Berry talks with Dr. Jim Albert, Professor Emeritus at Bowling Green State University, whose extensive work encompasses Bayesian statistics and computation, sports analytics, and decades of exemplary teaching. Dr. Albert shares insights on integrating sports into statistics education and discusses his transition from academic roots to consulting for the Houston Astros. This episode highlights the evolution of sports statistics—from manual data collection to sophisticated analytics—and critiques traditional metrics in favor of advanced systems. The dialogue explores career opportunities in sports statistics as well as the need for open research avenues in sports analytics, facilitating broader access and distribution of statistical insights.

Key Highlights

• Use of sports to contextualize statistical concepts, providing practical illustrations over abstract textbook issues
• Exposing misconceptions about randomness, streakiness, and “clutch ability” perpetuated by both public myths and sports simulations
• Analytical evolution from traditional metrics like batting average to advanced assessments like OPS and on-base percentage
• Regression-to-the-mean explained with sports scenarios and its analogous application in clinical trial progression
• Challenges in adopting a unified approach to teaching statistics given students’ diverse cultural and sports familiarity
• Barriers in publishing sports analytics research, prompting initiatives for accessible, open publications

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

In this episode of "In the Interim…", Dr. Scott Berry examines the concept of “digital twins” in clinical trials. He details how simulation of clinical trials is a direct analog of digital twin methodology, allowing for the in-silico modeling of the physical trial conduct, enrollment, dropouts, and patient outcomes under varied assumptions. Scott discusses model-based patient prediction and highlights scenarios where prediction of counterfactual outcomes can increase efficiency, particularly in rare disease or limited-data settings. He provides a systematic comparison of Unlearn’s PROCOVA neural network approach with traditional covariate adjustment, noting that proprietary models must demonstrate clear improvement over standard methods, which is unlikely. There is great potential in the simulation of many digital twins for a patient as a potential augmentation or substitute for controls.

Key Highlights

• Defines digital twins using NASA history and Wikipedia.
• Describes clinical trial simulation as a digital twin methodology.
• Examines patient-level model-based prediction and covariate adjustment.
• Compares Unlearn’s PROCOVA with traditional approaches.
• Highlights transparency and reproducibility concerns with proprietary algorithms.
• Asserts that future trial efficiency demands integration of predictive modeling with randomization and large external datasets.

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

In this episode of "In the Interim…", Dr. Scott Berry interviews Dr. Andrew Thomson, owner and lead consultant of Regnitio. Thomson discusses his academic progression from mathematics at Cambridge to a Master’s at Southampton and advanced study with Prof. Sylvia Richardson at Imperial College, followed by doctoral work in cluster randomized trials at the London School of Hygiene and Tropical Medicine. He recounts the realities of regulatory roles, including contemplative study of data, working within multidisciplinary teams, and delivering regulatory assessments to senior committees. The episode contrasts EMA’s collaborative cross-country structure against the more centralized FDA process and explores methodological challenges faced by both. Scott and Andrew discuss regulatory expectations for interim analyses, the definition and metrics of trial complexity, and differing approaches to Type I error control across agencies. The conversation also covers the rapid adoption and adaptation of platform trials during COVID-19, and the impact on trial evaluation frameworks. Concluding, Thomson explains the motivation for launching Regnitio, emphasizing how regulatory perspective and multidisciplinary insight can support informed decision-making throughout clinical development.

Key Highlights

• Academic and professional pathway: Cambridge, Southampton, Imperial College, London School of Hygiene and Tropical Medicine
• Roles as a statistical assessor: analysis, collaborative review, expert panel presentations
• EMA vs. FDA: consensus-driven versus centralized approaches, harmonization challenges
• Trial complexity, Interim analyses, and diversity in regulatory interpretations
• Adoption and practicalities of platform trials during the COVID-19 response
• Consulting goals: integrating regulatory perspective and broad expertise for drug development decisions

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

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele analyze how regulatory, editorial, and science community standards often impose additional, inconsistent requirements for novel methods in clinical trial design, rarely applied to standard approaches. Examples from oncology, enrichment trials, platform studies, and endpoint analysis illustrate how adaptive and Bayesian designs are frequently subject to higher scrutiny, shifting metrics, or distinct evidentiary demands. The episode covers technical and regulatory issues, such as the selective application of Type 1 error controls, evolving multiplicity guidance, and challenges in ethical reasoning with adaptive allocation. Scott and Kert frame the discussion with empirical comparisons and advocate for the use of clinical trial simulation to ensure fair, metric-driven evaluation of both novel and legacy designs.

Key Highlights:

• Oncology combination therapy trial with Bayesian borrowing facing heightened regulatory caution versus single-arm historical controls.
• Hierarchical versus pooled analysis in enrichment/basket trials, with focus on error definitions and subgroup effects that have always existed.
• ICH E20 guidance potentially discourages use of enrichment by imposing new subgroup comparison burdens absent from standard trials.
• Platform trial multiplicity rules contrasted with parallel single-arm trials; regulatory stance continues to evolve.
• Ethical debate on adaptive allocation: questioning rationale behind adaptive randomizing may be ethically challenging, but fixed allocation is okay despite same interim data.
• Critical review of explicit utility weighting in the DAWN trial, despite alternative methods having the same issues

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

In this episode of "In the Interim…", Dr. Scott Berry examines the mathematical foundations and efficiency claims of the promising zone design for adaptive sample size in clinical trials. Scott unpacks the conditional power thresholds that trigger sample size increases without the need to adjust alpha, as originally presented by Mehta & Pocock. He systematically demonstrates, via simulation, that the promising zone rarely provides meaningful efficiency gains over fixed designs and is consistently outperformed by group sequential designs that allocate alpha across multiple analyses. Using a driving-route analogy, Scott highlights the practical flaw in making pivotal trial decisions earlier than necessary due to arbitrary statistical rules rather than observing current data. He underlines that at Berry; simulation efforts have yet to reveal a scenario where the promising zone design is more efficient than a thoughtfully constructed group sequential or Goldilocks trial. The episode urges trialists to simulate, compare, and optimize—not to accept appealing mathematical tricks without rigorous evaluation.

Key Highlights

• Explanation of the promising zone’s conditional power mechanism and alpha control.
• Simulation-based comparison of power and average sample size across design types.
• Direct comparison of group sequential vs. promising zone designs.
• Discussion of futility rules and their impact on design choice.
• Commentary on Goldilocks designs for incomplete data.

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