EPISODE · Jun 30, 2025 · 39 MIN
A Statistician reads JAMA
from In the Interim... · host Berry
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.
What this episode covers
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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A Statistician reads JAMA
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