In the Interim...

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 Highlights
Real-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).
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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 Highlights
Gajewski 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 Highlights
Emphasizes 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.
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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 Highlights
Precise 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.
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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 Highlights
High 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).
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