In the Interim...

In this episode of "In the Interim…", Dr. Don Berry provides a detailed account of co-chairing the 1997 NIH consensus development conference on mammography for women in their 40s. His conversation with Dr. Scott Berry covers the statistical and clinical complexities of breast cancer screening, addressing lead time and length bias, trial design limitations. Don discusses the panel’s finding, based on randomized trials and meta-analysis, that the average benefit of screening women in their 40s is modest (an estimated 1.4-day average life extension and 18% hazard reduction). The panel recommended individualized decision-making rather than universal screening. The episode follows the reaction: heated debate with radiologists, scrutiny from journalists and policymakers, Senate testimony, and personal threats. Don explains how these findings became distilled into soundbites, evidenced by coverage in The New York Times, Chicago Tribune, and a reference in James Patterson’s Murder Games. The conversation addresses overdiagnosis, false positives, and the consequence some called the “Berry effect”—an observed drop in mammogram rates after the panel’s recommendations.
Key Highlights
Don Berry’s NIH consensus conference leadership and approach to breast cancer screening recommendations.
Statistical bias and trial limitations inherent in mammography evidence.
Meta-analysis findings: 18% hazard reduction; 1.4-day average life extension.
Policy guidance supporting individualized patient decision-making over universal screening.
Strong backlash from advocacy, media, radiology, and government—including Senate hearings and personal threats.
Enduring debates, public misunderstandings, and continued citation of these events in scientific and popular sources.
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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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