The trials of interpreting clinical trials - A Bayesian perspective Colchicine in secondary cardiovascular prevention
Bibliographic record
Abstract
Abstract Objectives Evidence based medicine (EBM) places systematic reviews and meta-analyses, at the top of the evidential pyramid. Bayesian methods may assist in better understanding uncertainties and improve interpretations and harmonization. Design A 2022 meta-analysis concluded that colchicine reduced the cardiac risk in secondary prevention. Nevertheless, a large, RCT (CLEAR) continued to randomize acute patients to colchicine or placebo and in 2025 published their findings of no benefit. Bayesian sequential analyses and hierarchical meta-analysis can inform the decision to complete this trial and augment the nuances surrounding its interpretation. Setting RCTs of coronary artery disease (CAD) patients with an acute coronary syndrome admission undergoing percutaneous coronary intervention (PCI). Interventions Randomization to colchicine or placebo. Main outcomes The primary outcome was major adverse cardiovascular events (MACE), a composite of death from cardiovascular causes, recurrent myocardial infarction, stroke, or unplanned ischemia-driven coronary revascularization. Results A published 2022 meta-analysis suggested a statistical MACE decrease with colchicine (RR 0.73 [95% confidence interval (CI) 0.62, 0.86]), but a Bayesian reanalysis showed a 95% credible interval (95% CrI 0.26, 1.70) for the next study, justifying continuing the CLEAR tiral. CLEAR results were eventually interpreted as “negative” (HR, 0.99; 95% confidence interval [CI], 0.85 to 1.16). Bayesian sequential re-analyses using a vague prior (i..e. result dominated by CLEAR), an all-inclusive prior (based on the previous meta-analysis), and a focused prior (considering only the largest and most similar previous RCT) showed 58%, 100% and 92% probabilities respectively of a MACE decrease with colchicine. The probabilities of clinically meaningful decreased, based on > absolute 15% MACE reduction, were more modest, between 2% - 41%. Conclusions Bayesian analyses offer advantages in clinical trial design and interpretation. The worked example strongly suggests some benefit for colchicine in secondary cardiovascular prevention, but it is unlikely to be of clinical importance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.567 | 0.811 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".