In the Interim...

• The Legend of I-SPY 2 - Part B

  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).

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• The Legend of I-SPY 2 - Part A

  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.

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• The STEP Platform with Dr. Eva Mistry and Dr. Jordan Elm

  This episode of "In the Interim..." features an in-depth discussion of the StrokeNet Thrombectomy Endovascular Platform (STEP), a multi-domain, multi-factorial, adaptive platform trial for acute stroke, anchored in the NIH StrokeNet network. Guests Dr. Eva Mistry (University of Cincinnati) and Dr. Jordan Elm (Medical University of South Carolina) join us to explain how STEP enables simultaneous investigation of multiple treatment strategies in patients with acute ischemic stroke. The conversation details the use of a master protocol, the integration of industry partners through the Other Transactional Authority (OTA) NIH mechanism, and innovative statistical designs to efficiently identify improved treatment strategies.Key Highlights:STEP utilizes a master protocol within NIH StrokeNet, unifying eligibility, procedures, and data collection across all study domains.The platform supports multiple research questions.In an initial domain STEP applies a statistical change-point model to empirically estimate the thresholds where EVT is effective, neutral, or potentially deleterious based on medium vessel occlusions and baseline clinical status. Protocols may be adapted in response to new external data, including pausing and revising enrollment in specific subpopulations when emerging science warrants.Shared control groups are used wherever applicable, improving trial efficiency by reducing the number of patients allocated to control arms and allowing eligible patients to contribute to multiple domains when protocol and scientific rationale permit.

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• 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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