In the Interim...

• A Statistician reads JAMA

  Dr. Scott Berry applies a statistician’s review of a random trial result published in JAMA – the FAIR-HF2 clinical trial.  Interrogating the frequentist paradigm and the focus on the binary outcome of the primary hypothesis test. He scrutinizes the Hochberg multiplicity adjustment, challenges the prevailing disregard for accumulated scientific evidence, and contrasts the limitations of black/white view of clinical trial of over 1000 patients and 6 years of enrollment. A contrast is made to what a potential Bayesian approach, grounded in practical trial interpretation and evidence integration would look like. The episode argues how current norms, created by dogmatic statistical views, in clinical trial analysis can obscure or perhaps mislead from meaningful findings and limit the utility of costly, complex studies.Key HighlightsFAIR-HF2 randomized 1,105 patients with heart failure and iron deficiency to intravenous ferric carboxymaltose or placebo across 70 sites, with three pre-specified co-primary analyses.The study relied on the Hochberg procedure to control family-wise error across analyses: (1) time to first cardiovascular death or heart failure hospitalization; (2) total heart failure hospitalizations; (3) time to first event in a highly iron-deficient subgroup.Results showed a favorable hazard ratio (0.79) and a p-value below 0.05 for primary composite 1, but statistical significance was nullified under Hochberg multiplicity criteria as other endpoints failed threshold requirements.Berry challenges the reduction of trial outcomes to discrete “significant” or “not significant” designations—critiquing the scientific and statistical culture that ignores gradient evidence in favor of only black-and-white outcomes.He details the likelihood principle and Bayesian analysis as superior frameworks, quantifying a 98% posterior probability of benefit; he contextualizes findings with prior evidence from the HEART-FID, IRONMAN, and AFFIRM-AHF trials and published meta-analyses—arguing that isolated, negative conclusions defy cumulative data.The discussion extends to the inefficiency of fixed trial designs, the missed value in adaptive methodologies, and the inefficacy of requiring full-scale repeat trials all analyzed in isolation, when evidence already points strongly to a beneficial effect.

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• Seamless 2/3 Trial Designs

  Scott Berry convenes co-authors Kert Viele, Joe Marion, and Lindsay Berry to discuss the statistical and developmental nuances of inferentially seamless phase 2/3 clinical trial designs. The group dissects the simple method for distributing alpha when including stage 1 data, whether it is a good idea to distribute alpha, and the optimal allocation of sample size when Stage 1 data are carried forward, all referencing their recently published work in Pharmaceutical Statistics.Key Highlights:Systematic definition of seamless phase 2/3 trial designs, contrasting fixed, separate-phase models with integrated, inferentially seamless approaches.Detailed explanation of the required alpha adjustment when selecting doses partway through—leveraging group sequential theory, normal approximations, and quadrature for explicit formula derivation; R code and calculation procedure are made available for practitioners.Exploration of the information fraction curve for adjusted alpha, emphasizing that initial adjustment is numerically significant but does not inherently reduce statistical power.Findings indicate that power is always higher when including stage 1 data – and outperforms a closed testing procedure.Discussion of when seamless trials may not be advantageous: operational and statistical limitations: insufficient endpoint/regulatory understanding for phase 3, differences in manufacturing readiness, need for public phase 2 results for funding, and proof of concept hurdles; identifies real scenarios where seamless 2/3 designs are challenging.Considerations for operational bias and blinding, with technical commentary on the boundaries of unblinding within company roles.Provision of practical R code and explicit analytic guidance for calculating adjusted alpha in seamless design protocols.

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• Spending Alpha

  In this solo episode of "In the Interim...", Scott Berry, President and Senior Statistical Scientist at Berry Consultants, addresses deep-rooted confusion in the field of adaptive clinical trial design surrounding the concept of “spending alpha.” Drawing on practical experience and rigorous statistical foundations, Berry addresses the prevailing language and myths that conflate interim analysis with loss of type I error. He clarifies that, with planned and transparent allocation of alpha, interim analyses enable more power with more efficient design, and robust clinical trials—without sacrificing statistical validity. This is a precise and fact-driven examination for those demanding technical clarity, not marketing gloss.Key HighlightsExplains the basics of hypothesis testing in superiority trials, highlighting why a one-sided 2.5% alpha is the operational standard despite persistent use of two-sided 5% language in clinical protocols.Refutes the widespread belief that reviewing interim data costs available alpha, making clear that statistical error is not “penalized”—it is allocated, with potential efficiencies in average sample size and, in thoughtfully extended designs, gains in operating characteristics such as power.Describes real-world examples, including the SEPSIS-ACT (selepressin) trial sponsored by Ferring Pharmaceuticals, which incorporated more than 20 interim analyses while maintaining a pre-specified final alpha of 0.025; underscores the necessity of transparent, prospective design and explicit documentation for regulatory acceptance.Distinguishes between interim actions—such as futility analyses or response-adaptive randomization, which require no alpha adjustment, and early efficacy analyses, which must be precisely modeled to preserve type I error.Challenges terminology like “penalty” and “spending alpha,” asserting that imprecise language fosters misunderstanding and leads to missed opportunities in adaptive trial efficiency.Emphasizes the crucial role of prospective, simulation-based planning and clear protocol definition at every interim, anchoring statistical practice in measured evidence, not historical convention.

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• Prof Craig Ritchie: Looking Back at EPAD, moving forward in Alzheimer's Disease

  Scott Berry, Founder of Berry Consultants, interviews Professor Craig Ritchie—specialist in brain health and neurodegenerative diseases, Chief Investigator of EPAD (European Prevention of Alzheimer Dementia), and CEO of Scottish Brain Sciences—for a broad discussion of platform trial methodology in Alzheimer’s Disease research as well as looking towards the future of drug development. The conversation dissects the origins and ambitions of the EPAD initiative, the conception and scientific function of the readiness cohort, and the pragmatic obstacles to deploying innovative trial models within rigid institutional frameworks. Professor Ritchie details why the EPAD platform trial failed to initiate any therapies, explores the fallout and industry shifts following COVID-19, and maps how Scottish Brain Sciences is directly applying these lessons—establishing the IONA readiness cohort to drive integration between clinical research and clinical practice. Key Highlights• Systematic review of EPAD’s objectives, specifically the platform trial and the development of a readiness cohort to streamline patient recruitment• Detailed account of practical barriers that prevented EPAD from launching interventional arms, including pharmaceutical sponsor reluctance, inflexible IMI funding mechanisms, and the inherent risk aversion surrounding novel platform structures• Discussion of participant contribution to research design and delivery—an early demonstration of patient involvement models now broadly recognized as best practice• Analysis of COVID-19's dual impact—derailing EPAD's momentum while catalyzing a change in industry and regulatory acceptance of platform trials in drug development• Tracing the origins and operationalization of the IONA readiness cohort at Scottish Brain Sciences, including direct integration of recruitment, biobanking, and engagement systems to address the translational gap in dementia medicine• Evidence-based critique of persistent use of conventional clinical trial formats in Alzheimer’s disease, dissecting operational, financial, and data limitations that stall progress

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• Drug Developers' Lessons from Sports: Regression-to-the-Mean

  In this engaging episode of "In the Interim...", host Dr. Scott Berry is joined by Dr. Nick Berry to explore the intriguing statistical parallels between sports and drug development, focusing on the concept of "regression-to-the-mean." Presenting examples that seem clear in sports, they discuss how these insights can illuminate the challenges faced in clinical trials and scientific inferences in medical decision making. Whether you're a statistician, drug developer, or sports enthusiast, this episode offers valuable perspectives on data interpretation and statistical phenomena.Key Highlights:• Discussion on how lessons from sports can benefit drug developers, emphasizing the concept of regression-to-the-mean.• Personal anecdotes from Scott and Nick's experiences, illustrating statistical learning through sports.• Examination of the regression-to-the-mean phenomenon through examples from baseball and golf.• Exploration of how misunderstanding the regression-to-the-mean can lead to poor decision-making in clinical trials.• Insights into placebo effects and how they are often confused with natural statistical phenomena.• How regression-to-the-mean impacts expectations in financial markets and personal finance decision-making.

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