Results of the COMPASS Trial Analyzed Using Win Ratio Compared With Conventional Analytic Approaches
Bibliographic record
Abstract
BACKGROUND: Win ratio (WR) is a newer analytic approach for trials with composite end points that accounts for the relative importance of individual components. Our objective was to compare the results of the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial analyzed using WR with those obtained using conventional statistical approaches. METHODS: We used an unmatched WR analysis for first and total (first plus recurrent) events to examine effects of rivaroxaban with aspirin and rivaroxaban alone vs aspirin alone on primary efficacy (cardiovascular death, stroke, myocardial infarction), safety (modified International Society on Thrombosis and Haemostasis major bleeding), and net clinical benefit (primary efficacy plus fatal or critical organ bleeding) end points. We compared the WR results with those obtained using the Cox proportional hazards regression model for first events and Anderson-Gill method for total events. We calculated the win difference to estimate absolute treatment effects. RESULTS: The WR approach produced results consistent with those obtained using conventional statistical methods for the primary composite end point (first event: WR, 1.32 [95% confidence interval (CI), 1.14-1.52]; 1/Cox hazard ratio, 1.32 [95% CI, 1.16-1.52]; total [first plus recurrent] events: WR, 1.32 [95% CI, 1.14-1.52]; 1/Anderson-Gill hazard ratio, 1.32 [95% CI, 1.16-1.54]) as well as for main safety and net clinical benefit end points. The absolute benefits of the combination of rivaroxaban and aspirin compared with aspirin alone calculated using the win difference were greatest in those with multiple high-risk features. CONCLUSIONS: Reanalysis of the COMPASS trial results using WR produced results that were consistent with those obtained using conventional statistical approaches. CLINICAL TRIAL REGISTRATION: NCT01776424.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".