Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and How Article Swipe
Samuel Heuts
,
Michał Kawczyński
,
Ahmed Sayed
,
Sarah Urbut
,
Arthur M. Albuquerque
,
John Mandrola
,
Sanjay Kaul
,
Frank E. Harrell
,
Andrea Gabrio
,
James M. Brophy
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.cjca.2024.11.002
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.cjca.2024.11.002
The Bayesian analytical framework is clinically intuitive, characterised by the incorporation of previous evidence into the analysis and allowing an estimation of treatment effects and their associated uncertainties. The application of Bayesian statistical inference is not new to the cardiovascular field, as illustrated by various recent randomised trials that have applied a primary Bayesian analysis. Given the guideline-shaping character of trials, a thorough understanding of the concepts and technical details of Bayesian statistical methodology is of utmost importance to the modern practicing cardiovascular physician. This review presents a step-by-step guide to interpreting and performing a Bayesian (re)analysis of cardiovascular clinical trials, while highlighting the main advantages of Bayesian inference for the clinical reader. After an introduction of the concepts of frequentist and Bayesian statistical inference and reasons to apply Bayesian methods, key steps in performing a Bayesian analysis are presented, including verification of the clinical appropriateness of the research question, quality and completeness of the trial design, and adequate elicitation of the prior (ie, one's belief toward a certain treatment before the current evidence becomes available); identification of the likelihood; and their combination into a posterior distribution. Examination of this posterior distribution offers not only the possibility of determining the probability of treatment superiority, but also the probability of exceeding any chosen minimal clinically important difference. Multiple priors should be transparently prespecified, limiting post hoc manipulations. Using this guide, 3 cardiovascular randomised controlled trials are reanalysed, demonstrating the clarity and versatility of Bayesian inference.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.cjca.2024.11.002
- OA Status
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- Cited By
- 15
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404136056Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.cjca.2024.11.002Digital Object Identifier
- Title
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Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and HowWork title
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reviewOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-07Full publication date if available
- Authors
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Samuel Heuts, Michał Kawczyński, Ahmed Sayed, Sarah Urbut, Arthur M. Albuquerque, John Mandrola, Sanjay Kaul, Frank E. Harrell, Andrea Gabrio, James M. BrophyList of authors in order
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https://doi.org/10.1016/j.cjca.2024.11.002Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.cjca.2024.11.002Direct OA link when available
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Medicine, Bayesian probability, Clinical trial, Intensive care medicine, Medical physics, Internal medicine, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 14, 2024: 1Per-year citation counts (last 5 years)
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