Referee report. For: A review of public health economic modelling in the National Institute for Health and Care Excellence (NICE) [version 1; peer review: 1 approved with reservations]
Bibliographic record
Abstract
Background : The National Institute for Health and Care Excellence (NICE) use economic modelling to inform judgements whenever further insight is required for decision-making. Doing so for public health guidance poses several challenges. The study’s objective was to investigate the level of heterogeneity in NICE’s public health economic models with regards to economic evaluation techniques, perspectives on outcomes and the measurement of non-health benefits. Methods : A review of all economic modelling reports published by NICE’s Centre for Public Health (CPH) as part of their guidance. Results: The review identified 56 eligible pieces of public health over the relevant period. Of these, 43 used economic modelling and 13 used no formal economic model. In total 61 economic models were used. Though the CPH specifies a reference case, in practice there is a large amount of variability from one model to the next. The most common perspective used for evaluations was that of the National Health Service (NHS); the most common economic evaluation approach was cost-utility analysis (CUA). 23 of the 56 topics used other combinations of perspective and technique, which allowed them to incorporate non-health effects, such as productivity, the effect on taxes raised and benefits spending, costs to the criminal justice sector, the effect on educational attainment and general wellbeing. Conclusions : NICE regularly updates its reference case, and non-CUA evaluation techniques have become more prominent in recent years. The results highlight the genuine advantages of having a variety of economic evaluation techniques available, which can be matched with the given topic. While it is always necessary to be wary of the possibility of gamesmanship and cherry picking, there is a surprising alignment between many approaches in certain circumstances.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.017 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.306 | 0.150 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".