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

Area of Science:

• Biostatistics
• Clinical Trials
• Medical Statistics

Background:

• The American Statistical Association (ASA) advises caution against overreliance on p-values and fixed significance thresholds.
• Rigid p-value thresholds (e.g., p < 0.05) can obscure statistical uncertainty and clinical relevance.
• Frequentist methods may not fully capture the nuances of treatment effect estimation.

Purpose of the Study:

• To illustrate how Bayesian methods can enhance the interpretation of randomized controlled trial (RCT) outcomes.
• To estimate the probability of treatment benefit using Bayesian approaches.
• To compare Bayesian and frequentist interpretations of trial results, particularly those near conventional significance levels.

Main Methods:

• Reanalysis of all-cause mortality data from two RCTs: EMPULSE and DanGer Shock.
• Application of Bayesian hierarchical random-effects models.
• Utilized both reference and data-derived priors for model fitting.

Main Results:

• For EMPULSE, high posterior probabilities for mortality benefit were observed (RR < 1: 90%-99%).
• For DanGer Shock, probabilities of mortality benefit were lower and more uncertain (RR < 1: 76%-98%).
• Despite similar frequentist p-values (0.04 and 0.05), Bayesian analysis revealed distinct levels of certainty regarding treatment efficacy.

Conclusions:

• Bayesian analysis provides more nuanced and decision-relevant insights compared to traditional p-value interpretation.
• The study highlights the value of Bayesian methods in quantifying the probability of treatment benefit.
• Bayesian approaches are particularly beneficial for interpreting trial results that fall close to conventional significance thresholds.