The biggest bang for the buck or bigger bucks for the bang: the fallacy of the cost-effectiveness threshold
Bibliographic record
Abstract
It has been suggested that scepticism among decision-makers about using cost-effectiveness analysis (CEA) is caused in part by the low level of the cost-effectiveness "thresholds" in the economic evaluation literature. This has led Ubel and colleagues to call for higher threshold values of US$200,000 or more per quality-adjusted life-year. We show that these arguments fail to identify the objective of CEA and hence do not consider whether or how the threshold relates to this objective. We show that incremental cost-effectiveness ratios (ICERs) cannot be used to identify an efficient use of resources--the "biggest bang for the bucks"--allocated to health care. On the contrary, the practical consequence of using the ICER approach is shown to be an increase in health care expenditures, or "bigger bucks for making a bang", without any evidence of the bang being bigger (i.e. that this leads to an increase in benefits to the population). We present an alternative approach that provides an unambiguous method of determining whether a new intervention leads to an increase in health gains from whatever resources are to be made available to health care decision-makers.
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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 | 0.105 | 0.334 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".