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Canadian Journal of Cardiology

Bayesian Analyses of Cardiovascular Trials—Bringing Added Value to the Table

  • James M. Brophy
    Correspondence
    Corresponding author: Dr James Brophy, Professor of Medicine and Epidemiology (McGill University), McGill University Health Centre, 1001 Decarie Blvd, Room C04.1410, Montréal, Québec H4A 3J1, Canada.
    Affiliations
    McGill University Health Centre, Montréal, Québec, Canada
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Published:March 25, 2021DOI:https://doi.org/10.1016/j.cjca.2021.03.014

      Abstract

      The limitations of traditional statistical analyses of randomised clinical trials that follow the frequentist inference paradigm have been increasingly noted. This article discusses the Bayesian approach to statistical inference in randomised 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 reanalysed 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 synthesise and update our knowledge base. Ultimately, it is argued that the Bayesian approach is more intuitive and 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.

      Résumé

      Les limites des analyses statistiques classiques selon le paradigme d'inférence fréquentiste lors des essais cliniques randomisés sont de plus en plus mises en évidence. Cet article traite de l'approche bayésienne de l'inférence statistique dans les essais cliniques randomisés, en démontrant son fonctionnement, son utilité et ses limites par l'examen d'exemples d'essais cardiovasculaires actuels. Un aperçu simplifié des mécanismes de l'inférence bayésienne et un glossaire de la terminologie bayésienne sont d'abord fournis. La dualité de l'approche bayésienne, qui fournit à la fois un calcul probant fondé sur le rapport de vraisemblance et un calcul axé sur les convictions, combinant nos convictions antérieures aux données actuelles, est présentée. Des essais cardiovasculaires spécifiques sont analysés de nouveau à l'aide des méthodes bayésiennes. On affirme que l'approche bayésienne, en étant mieux en mesure d’évaluer et de modéliser l'incertitude, permet de mieux comprendre la force et la quantification des données probantes, la distinction entre la signification statistique et la signification clinique, la variabilité au sein d'un même essai et entre les essais, les analyses de sous-groupes, l'utilité des valeurs a priori informatives ainsi que notre capacité à synthétiser et à mettre à jour notre base de connaissances. En définitive, l'approche bayésienne est plus intuitive et transparente, permet une analyse et une interprétation améliorées des données et peut conduire à une meilleure prise de décision non seulement par les investigateurs, mais aussi par les cliniciens, les rédacteurs de lignes directrices et même les experts-conseils en réglementation.
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