Mortality Benefit of Alirocumab: A Bayesian Perspective
Bibliographic record
Abstract
Background The ODYSSEY OUTCOMES (Alirocumab and Cardiovascular Outcomes after Acute Coronary Syndrome) trial demonstrated that alirocumab reduced major cardiovascular events. However, because of the hierarchical testing strategy used for the multiple outcomes examined, the observed reduction in all-cause mortality was labeled "nominally significant" which has clouded its interpretation. Methods and Results We re-analyzed data from ODYSSEY OUTCOMES using Bayesian methods and generated various prior probabilities by incorporating mortality data from previous similar PCSK9 (proprotein convertase subtilisin-kexin type 9) inhibitor trials. We first used data from the ODYSSEY OUTCOMES trial with a non-informative prior, then sequentially added data from ODYSSEY LONG TERM and the FOURIER trial, giving FOURIER full weight, 50% weight and 10%. The posterior probability of a mortality reduction using only the ODYSSEY OUTCOMES data was hazard ratio 0.85 (95% CI 0.74-0.99) which corresponded to a 98.4% probability of a mortality benefit. When the ODYSSEY LONG TERM data were added to the analysis, the posterior probability was hazard ratio 0.84 (95% CI 0.72-0.97) with a 99.9% probability of mortality reduction, and when the FOURIER data were added to the analysis the posterior probability was hazard ratio 0.94 (95% CI 0.85-1.04) with an 89.1% probability of a mortality reduction. When the FOURIER trial was given only 50% or 10% weight, the probability of a mortality reduction rose 95.4% and 98.7%, respectively. We estimate that the probability of >1% absolute risk reduction ranges from 8% to 24%, while the probability of >0.5% absolute risk reduction ranges from 66% to 89%. Conclusions Our analysis demonstrates a high likelihood that alirocumab confers a reduction in all-cause mortality, despite the equivocal interpretation of the data in the original ODYSSEY OUTCOMES publication.
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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 | 0.038 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".