Probabilistic One-Way Sensitivity Analysis with Multiple Comparators: The Conditional Net Benefit Frontier
Bibliographic record
Abstract
Although there have been substantial developments in the analysis of uncertainty in economic evaluations of health care programmes, the development of methods for one-way sensitivity analysis has been notably slower. Conditional incremental net benefit was recently proposed as an approach for implementing probabilistic one-way sensitivity analysis for economic evaluations comparing two strategies. In this paper, we generalise this approach to economic evaluations that compare three or more strategies. We find that 'conditional net benefit' may be used to conduct probabilistic one-way sensitivity analysis for economic evaluations comparing any number of strategies. We also propose the 'conditional net benefit frontier', which may be used to identify the most cost-effective of any number of strategies conditional upon the specific value of a parameter of interest.
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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 | 0.131 | 0.194 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".