Technical Note: Acceptability Curves Could Be Misleading When Correlated Strategies Are Compared
Bibliographic record
Abstract
T recent discussions1 3 on the limitation of the cost-effectiveness acceptability curves (CEACs) was helpful in reevaluating the value of CEACs in cost-effectiveness studies. One of the limitations of the CEACs, as pointed out by Groot Koerkamp and colleagues1 as well as other authors,4,5 is that they may mislead policy makers regarding the preferred alternative. This refers to the fact that the ranking of strategies based on CEAC might be different from their ranking based on expected net benefit. This known phenomenon has so far been attributed to the higher positive skew of the distribution of the incremental net benefit (INB) of the optimal strategy.1,4,5 We would like to point out that CEACs can also be misleading when correlated strategies are compared. To be explicit, let us imagine that the decision maker is interested in a model-based cost utility analysis of 2 new strategies as compared with a current strategy for treating a malignancy. Strategy 0 is the current (baseline) practice, strategy 1 is based on the new drug A, and strategy 2 is based on drug A plus the palliative drug B, with slightly additional costs and small increase in quality of life. Assume that the output of the model can be approximated using the following distributions:
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".