In the Interim...

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 legitimacyAnalogies from soccer, golf, baseball, and wrestling on the societal role of rules and enforcementClassic statistics lessons on missing data, inference, and survivorship biasDiscussion of post-hoc versus prespecified analysis and its implications in trial integrityDefense of adaptive and Bayesian methodology as scientifically valid through pre-specification and covariate adjustmentReflection on the ongoing evolution of fairness and rigor in sport and scienceFor 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 HighlightsDefinition and quantification of bias in early-stopped clinical trialsMathematical examples demonstrating bias magnitude in fixed and adaptive group sequential designsDetailed critique of the methodology and conclusions in Bassler et al. (JAMA 2010)Discussion correcting common misunderstandings in bias estimation and selective reportingSimulation as a decisive tool for precise bias estimationRegulatory context including FDA guidance and ICH E20 draft guidanceReference to DAWN trial as evidence of practical benefits of early stoppingFor 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 HighlightsTRACK: 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 HighlightsDefinition and mechanics of RAR, with interim analysis guiding allocation updatesMulti-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 literatureECMO/play-the-winner: risks of poorly simulated RARNecessity for rigorous pre-trial simulation and robust data flowsContextualization of RAR’s role in both traditional and learning healthcare environmentsFor 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 HighlightsResponse to H1N1 and feckless pandemic trialsInternational strategy meetings—origins of platform conceptPREPARE consortium and cross-continental fundingModular master protocol, factorial allocation, and domain-specific appendicesBayesian triggers and response adaptive randomizationPivot to COVID-19 and rapid data generationMulti-platform international collaborationFor 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 HighlightsAnalyzes 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 HighlightsRationale 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 HighlightsIntegration 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 HighlightsDeconstruction 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 HighlightsAI 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 HighlightsDissects sports futility rules (10-run rule, golf cuts, Bill James heuristic) and their application to clinical trial designArgues for prospective simulation to define adaptive futility thresholdsExplains how Bayesian predictive probability provides a more robust framework than conditional probability for interim adaptive decisionsDetails how aggressive futility criteria may prematurely stop trials and risk missing beneficial treatments, as in the aducanumab caseExplores 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 HighlightsDiscussion 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 HighlightsReal-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 HighlightsGajewski 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 HighlightsEmphasizes 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 HighlightsPrecise 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 HighlightsHigh 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 HighlightsPregnancy 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 HighlightsCassidy 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 HighlightsStructural 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 HighlightsSlightly 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 HighlightsMAPT as a scalable, master protocol for orthopaedic intervention evaluationHierarchical, patient-centered endpoint (survival, 4-level ambulation, days alive/out of hospital), analyzed with a Bayesian-modeled, non-parametric win ratioDomain-specific adaptation thresholds based on clinical differentiationInterim analyses after 100 patients, then every 50, informing early adaptation40 sites across US, Canada, and Europe, centralized data management at McMasterA unified DSMB structure with capacity for domain-specific expertise as neededTiered protocol access: open sharing, collaboration, direct integrationInfrastructure enables rapid domain addition and multi-investigator participationFor 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 HighlightsDr. 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 HighlightsClear 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-trialsFor 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 HighlightsIllustrated 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 HighlightsRegulatory 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 HighlightsDr. Devenport’s academic and geographic move from the US to EuropeResponsibilities in methods development, collaboration, and outreach at RocheContrasts in US and European pharmaceutical statistics culturesMeasured perspective on AI’s effect on statisticians’ responsibilitiesPractical 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 HighlightsArena 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 HighlightsStatistical structure and efficiency of seamless Phase II/III trial designsAdministrative (financial) interim analysis setup as funding decision triggers, distinct from futility analysesFDA operational bias guidance and requirements for trial blindingPredictive probability and assurance as objective interim criteriaSample data and simulation outputs to facilitate stakeholder alignmentFor 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 HighlightsStatistical education in Switzerland, bridging theory and early applied cancer trial experienceMove from academic consulting to a trial statistician role at Roche, emphasizing structured problem-solving in drug developmentApproach to predictive probability and assurance, balancing Bayesian and frequentist tools with strict emphasis on practicalityFormation of professional special interest groups with EFSPI and PSI, stepping in to address unmet community needs rather than seeking formal leadershipPerspective on Merck’s unified quantitative department, designed to remove silos and leverage interdisciplinary expertiseCautious view of AI as a complement to specific tasks, but not yet a replacement for nuanced clinical trial design or regulatory-facing strategiesCurrent focus on expanding causal inference methods and multi-state modeling for improved trial efficiency and evidence synthesisFor 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 methodsEthical tension in trial conduct, referencing the Belmont Report and equipoiseAdvances in computation and Markov Chain Monte Carlo (MCMC)Regulatory frameworks for Bayesian adaptive trials, including FDA guidanceImplementation details from I-SPY 2 and REMAP-CAP platform trialsBayesian methodology in the context of artificial intelligence, precision medicine, and future data integrationFor 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 HighlightsEarly 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 HighlightsAlzheimer’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 HighlightsUse of sports to contextualize statistical concepts, providing practical illustrations over abstract textbook issuesExposing misconceptions about randomness, streakiness, and “clutch ability” perpetuated by both public myths and sports simulationsAnalytical evolution from traditional metrics like batting average to advanced assessments like OPS and on-base percentageRegression-to-the-mean explained with sports scenarios and its analogous application in clinical trial progressionChallenges in adopting a unified approach to teaching statistics given students’ diverse cultural and sports familiarityBarriers in publishing sports analytics research, prompting initiatives for accessible, open publicationsFor 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 HighlightsDefines 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 HighlightsAcademic and professional pathway: Cambridge, Southampton, Imperial College, London School of Hygiene and Tropical MedicineRoles as a statistical assessor: analysis, collaborative review, expert panel presentationsEMA vs. FDA: consensus-driven versus centralized approaches, harmonization challengesTrial complexity, Interim analyses, and diversity in regulatory interpretationsAdoption and practicalities of platform trials during the COVID-19 responseConsulting goals: integrating regulatory perspective and broad expertise for drug development decisionsFor 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 issuesFor 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 HighlightsExplanation 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/

Episode 30 of “In the Interim…” features Dr. Janet Wittes, Fellow of the American Statistical Association, past president of the Society of Clinical Trials, and founder of Statistics Collaborative, in discussion with Dr. Scott Berry. Dr. Wittes details her progression from Radcliffe biochemistry to Harvard statistics, shaped by targeted mentorship and her family’s insistence on advanced scientific training. She describes teaching at Hunter College, her NIH/NHLBI tenure overseeing extensive DSMB work, and the launch of Statistics Collaborative 32 years ago, building the business with her children and their peers. The episode explores her consulting on clinical trial design for orphan and neglected diseases—malaria, dengue, leishmania, ALS—and vaccine development, with technical commentary on adaptive trial methods, operational issues in low-resource contexts, and decision-making for small-sample trials. Dr. Wittes reflects on statistical leadership, ongoing DSMB involvement, and the importance of evidence-driven public health. She underscores the need for contextual and cultural awareness in trial design, illustrated by her Lilith magazine story on kosher certification and challenges in stakeholder understanding. Discussion covers career obstacles, the evolution of clinical science, vaccine advocacy, and the critical role of diversity and practical on-site knowledge in advancing statistical research.Key HighlightsEarly academic transition from biochemistry to statistics.Serendipitous transition from academic career at Hunter College to Branch Chief of biostatistics at NIH/NHLBI.Founding Statistics Collaborative, business growth with children, and specialization in orphan disease trials.Consulting expertise in adaptive design, small-sample challenges, tropical and vaccine studies.Continued advocacy for vaccines, scientific rigor, and ethical public health practice.Importance of representation and context in science, demonstrated by real-world consulting examples.

In this episode of "In the Interim…", Dr. Scott Berry chats with Frank Harrell, a professor of Biostatistics at Vanderbilt University and W.J. Dixon Award winner. Harrell describes his transition from frequentist to Bayesian clinical trial design, prompted by a decisive meeting with Dr. Don Berry, informed by David Spiegelhalter’s published work. The dialogue addresses persistent academic opposition to Bayesian methods, operational constraints in trial implementation, regulatory work at FDA, and technical Bayesian modeling details.Key HighlightsHarrell credits Don Berry’s direct influence for converting him to Bayesian methods during his early career at Duke, despite entrenched academic resistance.Discusses early cardiovascular research at Duke, experiences with large multicenter trials, and later founding Vanderbilt’s Biostatistics department.Details the compromise of using Bayesian interim monitoring and frequentist primary analyses under NIH and regulatory mandates.Outlines design and publication of the ORBITA cardiovascular trial (Imperial College London), using all-Bayesian longitudinal ordinal methodology—Lancet reviewers required all analyses remain Bayesian, rejecting inclusion of a mixing frequentist and Bayesian analyses.Critiques simulation of Type 1 error within Bayesian trial designs.Addresses deficiencies in eliciting utilities for clinical endpoints, underscoring operational challenges in longitudinal ordinal modeling and ethical imperatives for efficient early stopping.

In this episode of “In the Interim…”, Dr. Scott Berry interviews Dr. Derek Angus, Distinguished Professor and Chair of Critical Care Medicine at the University of Pittsburgh and Senior Editor at JAMA. The discussion addresses the decades-long controversy surrounding steroid use in community-acquired pneumonia (CAP) and sepsis. The episode delivers a chronological assessment of the evidence base—summarizing trial results from pivotal studies, including CAPE COD, REMAP-CAP, ADRENAL, and multiple French trials led by Dr. Djillali Annane. Dr. Angus analyzes why discrepancies persist in outcomes, clinical recommendations, and international guidelines, and underscores the challenge of heterogeneous treatment effects. The episode closes with an argument for adaptive trial designs, Bayesian inference, and embedded randomization within learning health systems as critical tools for clarifying complex response patterns and improving patient care.Key HighlightsHistorical evolution of clinical trials studying steroid regimens for CAP/sepsisReview of CAPE COD, REMAP-CAP, ADRENAL, and Annane-led French trials showing conflicting signals.Discussion of persistent heterogeneity in trial populations, interventions, and endpoints.Identification of methodological limitations—control contamination, endpoint definitions, varying inclusion criteria.Exploration of Bayesian and adaptive trial design, and operationalization of learning health systems to resolve evidence gaps.For more, visit: https://www.berryconsultants.com/

Dr. Scott Berry hosts this episode of "In the Interim…", opening with statistical analysis of elite athletes before focusing on the misunderstood role of clinical trial simulation. He distinguishes simulation as a predictive tool from its use as an in-silico process that enables trial design exploration, iteration, and optimization. Clinical trial simulation provides a mechanism for iterative comparison of multiple designs, driven by ongoing team feedback and evolving trial objectives. Scott stresses that rigid simulation plans are “not productive,” since the most effective designs typically emerge when stakeholders view real trial examples and suggest new design options in real time. The ICECAP trial serves as a key illustration, where the final design was shaped by simulation-informed team input across multiple iterations, from three tested durations to ten with response adaptive randomization. Scott also discusses the creation of the FACTS software, highlighting its ability to test alternative designs rapidly, present side-by-side comparisons, and conduct counterfactual analyses—revealing what different trial configurations would have produced using the same simulated datasets.Key HighlightsSimulation contrasted as a predictive tool versus engine for iterative design evaluation.Emphasizes design process as team-driven and iterative, not prescriptive.Use of concrete example trials enhances communication across multidisciplinary teams.FACTS software enables design flexibility, in silico iteration, and comparative scenario analysis.ICECAP trial as an instance of simulation-informed design adaptation.For more visit: https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele review the ICH E20 draft guidance on adaptive clinical trial designs, offering a technical yet accessible breakdown for trial sponsors, practitioners, and those interested in clinical development. Drawing on their practical experience in creating and presenting adaptive trial designs to regulators, they discuss the document’s strengths, areas of consensus, and where cautionary or restrictive language appears. Listeners are guided through the evolving regulatory landscape, distinctions between Bayesian and frequentist approaches, and what new harmonization efforts mean for planning adaptive confirmatory trials. The episode conveys hands-on examples, such as the Sepsis ACT seamless trial and the ROAR pan-tumor trial, illustrating technical points with real-world context. Key operational topics—blinding, operational bias, adaptive design reports, and clinical trial simulations—are addressed. The discussion includes practical advice on navigating regulatory dialogue, limitations of ICH E20 in early-phase or nontraditional designs, and the necessity of clear, justification for adaptive (complex) trial features.Key HighlightsICH E20 as a global regulatory framework for adaptive designsTone and caution in guidance may shape sponsor interpretationSeamless, Bayesian, and enrichment all confirmatory trialsOperational guidance: reporting, simulation, interim, and blinding requirementsEmphasis on justification and transparent communication with regulatorsFor more, visit: https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry and NIH’s Dr. Michael Proschan conduct a detailed discussion from opposing viewpoints on response-adaptive randomization (RAR) in clinical trials. The discussion focuses on where they agree – on the positives and negatives of RAR, and where they disagree on its scientific use. Key HighlightsPotential issues of using RAR: Potential temporal trends, unblinding, reduction in statistical efficiency in 2-arm trialsPotential benefits include improved statistical efficiency in multi-arm trials  depending on the goals (e.g. dose-finding trials).Potential unblinding of results in non-blinded trials and the need for operational excellence.Ethical and Bayesian perspectives are considered, but emphasis remains empirical.For more visit: https://www.berryconsultants.com/

In this episode of "In the Interim…", Dr. Scott Berry, Dr. Elizabeth Lorenzi, and Dr. Amy Crawford discuss the STEP platform trial’s statistical methodology for evaluating which acute stroke patients benefit and which do not from endovascular therapy (EVT). The discussion critiques the inadequacy of traditional clinical trials powered for a single population to show benefit, as the goal of the trial is to identify who benefits, not if the entire population has a net benefit. The team walks through the development and simulation of a Bayesian change point model, addressing heterogeneous treatment responses across the NIH Stroke Scale. The adaptive platform design leverages scheduled interim analyses to draw timely, data-driven conclusions about patient subgroups, improving trial efficiency and relevance. The episode also previews scaling to two-dimensional modeling, incorporating both stroke severity and time since last known well, and emphasizes ongoing clinical trial simulation and close integration between clinicians and statisticians throughout trial design and execution.Key HighlightsSTEP platform master protocol and the NIH StrokeNet collaborative infrastructureClinical rationale for Bayesian change point modeling of the effect of EVT across the patientsShift from single to dual change point models to reflect regions of equivalenceDevelopment of custom C code and MCMC samplers due to limits of standard toolsInterim analyses direct adaptive enrollment and define actionable conclusionsFuture extensions to multidimensional change point curves modeling 

This episode of "In the Interim…" features Dr. Scott Berry, Dr. Kert Viele, and Dr. Melanie Quintana of Berry Consultants dissecting the technical and operational landscape of Bayesian statistics in clinical trial design. The episode discussed what is Bayesian statistics, the impact of informative and non-informative priors, and clarifies when and why Bayesian approaches surpass frequentist analyses—especially in adaptive, platform, and rare disease trial settings. The discussion directly challenges the misconception that Bayesian methods “lower the bar," presenting evidence that they often require broader data synthesis and can raise evidentiary standards.Key regulatory developments at FDA and EMA are reviewed, with attention to updated guidance and increased adoption. Case studies illustrate Bayesian methods in practice, including the prospectively combined phase 2 and 3 analysis for REBYOTA approval; hierarchical modeling in GNE myopathy; shared controls and endpoint integration in the HEALEY ALS Platform Trial; and robust subgroup borrowing in the ROAR basket trial. The team also addresses technical challenges such as multiplicity, subgroup analysis, complexity in endpoint modeling, and appropriate strategies for blending Bayesian and frequentist approaches for maximum regulatory and scientific clarity.Key HighlightsClear explanation and real-world examples of Bayesian analysis in clinical trials.Theoretical and practical distinctions from frequentist methodsPractical breakdown of control sharing, endpoint integration, and subgroup borrowing.Regulatory position and the increasing acceptance of Bayesian trial designs and analyses.Case examples: REBYOTA, GNE myopathy, HEALY ALS Platform Trial, ROAR basket trial.

Dr. Scott Berry and Dr. Kert Viele discuss the origins and implementation of the “time machine” modeling approach, beginning with sports analytics and progressing to adaptive platform clinical trials. The episode focuses on how techniques for comparing athletes across eras translate into methodology for platform trials. Key HighlightsSports analytics as foundation: Early work of modelling athlete comparisons across eras using bridging methodologies.Platform trial application: The time machine model in I-SPY 2 enabled efficient control allocation through overlapping arms over extended trial periods.Core modeling principles: Additive treatment effect assumptions and the necessity of sufficient temporal overlap for reliable era comparisons.Statistical implementation: Approaches include categorical era adjustment and Bayesian smoothing splines for modeling change over time.Limitations and disease specificity: In conditions with rapid clinical or epidemiologic change, such as COVID-19, non-concurrent controls are avoided due to high risk of era by treatment interaction.Regulatory and methodological distinction: The model leverages within-trial overlapping data collected under a unified protocol, contrasting sharply with external or historical controls.

In this episode, Dr. Don Berry and Dr. Scott Berry provide an in-depth account of I-SPY 2, focusing on the trial’s use of the “time machine” methodology—a Bayesian solution allowing bridging across arms to inform ongoing analyses. The discussion details how predictive probabilities and adaptive randomization shaped pivotal decisions, including the handling of Pertuzumab’s approval and Neratinib’s subtype-specific performance. This episode also documents the technical and operational contributions of Laura Esserman, Anna Barker, Janet Woodcock, Meredith Buxton, and Ashish Sanil, clarifying the roles that enabled the platform’s success and broader impact on subsequent adaptive trials.Key HighlightsIntroduction of the “time machine” concept, enabling valid comparison between experimental and control arms even when enrollment periods differ—a pragmatic solution originally utilized in sports examples for evolving platform trials as treatments and control arms change.Ongoing trial conduct driven by a Bayesian adaptive algorithm, developed and maintained by Berry Consultants statisticians, which computes predictive probabilities to guide arm graduation, futility, and real-time adjustment of randomization probabilities.Neratinib serves as a case study in subtype-specific adaptive randomization: the platform set randomization probability to zero in subtypes without signal, while effective subtypes increased randomization and advanced to graduation.I-SPY 2’s methodologies shaped subsequent adaptive platform trials (GBM AGILE, Precision Promise, COVID-19 ACTIV networks), with regulatory acceptance reflected in FDA guidance and Janet Woodcock’s public recognition of adaptive randomization as “adequate and well controlled” for registration studies.Specific recognition: Laura Esserman (trial leadership), Anna Barker (funding and strategic input), Janet Woodcock (FDA guidance and adaptive methods support), Meredith Buxton (logistics; GCAR leadership), and Ashish Sanil (Berry Consultants; ongoing algorithm implementation).

In Episode 20 of Berry’s "In the Interim..." Podcast, The Legend of I-SPY 2 - Part A, Dr. Don Berry and Dr. Scott Berry discuss the origins and design of the I-SPY trials. Their conversation explains the inefficiency of traditional adjuvant breast cancer trials and details the shift to the neoadjuvant approach, where tumor response can be observed prior to surgery. I-SPY 1 served as a proof-of-concept using MRI for probabilistic prediction of pathologic complete response (pCR). I-SPY 2 represents a major advancement in clinical trial science, introducing a multi-arm bandit methodology, integration of biomarker-driven subtypes and signatures, and a structured funding model that transitioned from philanthropy to “pay to play” industry support.