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 Highlights

• FAIR-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.

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.

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 Highlights

• Explains 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.