A quantitative evaluation of the regulatory assessment of the benefits and risks of rofecoxib relative to naproxen: an application of the incremental net‐benefit framework
Bibliographic record
Abstract
PURPOSE: To undertake a quantitative benefit-risk analysis of rofecoxib relative to naproxen using an incremental net-benefit (INB) analysis from the societal perspective, using the same data evaluated by the Health Canada and US FDA expert advisory panels. METHODS: We developed a discrete event simulation model to calculate the INB of rofecoxib relative to naproxen in arthritis patients over a 1-year time horizon. All outcomes were weighted using societal utilities for each health state which facilitated the use of quality-adjusted life years (QALYs) as the outcome. Probability distributions were incorporated for each model parameter to facilitate a probabilistic analysis using second-order Monte Carlo simulation. RESULTS: In the base case analysis, the mean INB (SD) of rofecoxib relative to naproxen was 0.0002 (0.415) QALYs per patient over 12 months of treatment, or 0.2 QALYs per 1000 patients treated. The probabilistic sensitivity analysis resulted in a mean INB of 0.0022 QALYs (95%CI -0.0005, 0.0051). Overall, the INB associated with rofecoxib relative to naproxen was ≥0 in 94% of the iterations of the model. CONCLUSIONS: This analysis illustrates the application of the incremental net-benefit framework to quantitative benefit-risk evaluation, and suggests that the potential benefits of rofecoxib outweigh the potential harms relative to naproxen over 1 year from the societal perspective under the assumptions of this model.
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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 | 0.046 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".