Cost‐effectiveness analysis when the WTA is greater than the WTP
Bibliographic record
Abstract
The incremental cost effectiveness ratio has long been the standard parameter of interest in the assessment of the cost-effectiveness of a new treatment. However, due to concerns with interpretability and statistical inference, authors have suggested using the willingness-to-pay for a unit of health benefit to define the incremental net benefit as an alternative. The incremental net benefit has a more consistent interpretation and is amenable to routine statistical procedures. These procedures rely on the fact that the willingness-to-accept compensation for a loss of a unit of health benefit (at some cost saving) is the same as the willingness-to-pay for it. Theoretical and empirical evidence suggest, however, that in health care the willingness-to-accept is about twice as much as the willingness-to-pay. We use Bayesian methods to provide a statistical procedure for the cost-effectiveness comparison of two arms of a randomized clinical trial that allows the willingness-to-pay and the willingness-to-accept to have different values. An example is provided.
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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 | 0.062 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".