Samuel Heuts MD, PhD , Michal J. Kawczynski MD , Ahmed Sayed MD , Sarah M. Urbut MD, PhD , Arthur M. Albuquerque MD , John M. Mandrola MD , Sanjay Kaul MD , Frank E. Harrell Jr. PhD , Andrea Gabrio PhD , James M. Brophy MD, PhD
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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 <em>post hoc</em> manipulations. Using this guide, 3 cardiovascular randomised controlled trials are reanalysed, demonstrating the clarity and versatility of Bayesian inference.</div></div>","PeriodicalId":9555,"journal":{"name":"Canadian Journal of Cardiology","volume":"41 1","pages":"Pages 30-44"},"PeriodicalIF":5.8000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and How\",\"authors\":\"Samuel Heuts MD, PhD , Michal J. Kawczynski MD , Ahmed Sayed MD , Sarah M. Urbut MD, PhD , Arthur M. Albuquerque MD , John M. Mandrola MD , Sanjay Kaul MD , Frank E. Harrell Jr. PhD , Andrea Gabrio PhD , James M. Brophy MD, PhD\",\"doi\":\"10.1016/j.cjca.2024.11.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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. 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Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and How
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.
期刊介绍:
The Canadian Journal of Cardiology (CJC) is the official journal of the Canadian Cardiovascular Society (CCS). The CJC is a vehicle for the international dissemination of new knowledge in cardiology and cardiovascular science, particularly serving as the major venue for Canadian cardiovascular medicine.