Bayesian Reanalysis of Mortality Outcomes in Cardiovascular Trials: Addressing Limitations of Traditional Significance Testing.

The American Statistical Association (ASA) has cautioned against overreliance on p-values, noting that rigid thresholds (e.g., p < 0.05) can obscure statistical uncertainty and clinical meaning. This study illustrates how Bayesian methods can enrich trial interpretation by estimating the probability of treatment benefit. We reanalyzed all-cause mortality outcomes from two randomized trials-EMPULSE (empagliflozin in acute heart failure) and DanGer Shock (microaxial flow pump in cardiogenic shock)-using Bayesian hierarchical random-effects models with reference and data-derived priors. For EMPULSE, posterior probabilities of mortality benefit were high (RR < 1: 90%-99%; RR < 0.85: 72%-86%). In DanGer Shock, they were lower and more uncertain (RR < 1: 76%-98%; RR < 0.85: 15%-72%). Although both trials had similar frequentist p-values (0.04 and 0.05), Bayesian analysis revealed differing levels of certainty. These results highlight the value of Bayesian approaches in providing more nuanced, decision-relevant insights, particularly when trial results lie near conventional significance thresholds.