A Clinician's Guide to Correct Cost-Effectiveness Analysis: Think Incremental Not Average
Bibliographic record
Abstract
OBJECTIVE: To explain how to correctly report the results from a cost-effectiveness analysis (CEA). METHODS: Results were used from a hypothetical clinical trial to illustrate how different ways of reporting economic results affect both presentation of findings and formulation of conclusions. To provide context, we reviewed some high-profile exchanges in the scientific literature. RESULTS: The critical issue with which decision makers must grapple involves the trade-offs introduced by a new treatment or intervention. Specifically, are decision makers willing to pay the additional cost for the additional outcomes? This question cannot be considered without estimates of the additional cost and additional outcomes. Correct cost-effectiveness measures, such as the incremental cost-effectiveness ratio or the incremental net benefit, address this issue. CONCLUSIONS: As decision makers face the challenge of balancing increasing health care demand with cost containment, it will be crucial to identify cost-effective ways of providing care. Health care providers and other decision makers should not be misled by the results of improperly reported CEAs. Decisions around adoption of pharmaceuticals or implementation of new programs or interventions may be affected by which cost-effectiveness summary measure is reported. Thus consumers of CEA must have a basic understanding of why different methods give different results, and how the results should be interpreted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.109 | 0.374 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.013 | 0.035 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".