Bayesian Analytical Methods in Cardiovascular Clinical Trials: Why, When, and How
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.152 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it