Bayesian analyses of cardiovascular trials - bringing added value to the table.

Abstract

The limitations of traditional statistical analyses of randomized clinical trials which follow the frequentist inference paradigm have been increasingly noted. This article discusses the Bayesian approach to statistical inference in randomized clinical trials, demonstrating its functioning, utility, and limitations through an examination of current cardiovascular examples. A simplified overview of the mechanics of Bayesian inference and a glossary of the Bayesian terminology is first provided. The duality of the Bayesian approach providing both an evidential calculus based on the likelihood ratio and a belief calculus that incorporates our prior beliefs with the current data is presented. Specific cardiovascular trials are re-analysed with Bayesian methods. It is claimed that the Bayesian approach by providing an enhanced ability to appreciate and model uncertainty leads to an enriched understanding of the strength and quantification of the evidence, of the distinction between statistical and clinical significance, of the within and between trial variability, of subgroup analyses, of the utility of informative priors and of our ability to synthesize and update our knowledge base. Ultimately, it is argued that the Bayesian approach is more intuitive, transparent, permits enhanced data analysis and interpretation, and may lead to improved decision making not only by trialists but also by practicing clinicians, guideline writers, and even expert regulatory advisory consultants.