In the Interim...

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