In the Interim...

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
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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
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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.
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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.

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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
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