Use of evidence in decision models: an appraisal of health technology assessments in the UK since 1997
Bibliographic record
Abstract
OBJECTIVES: To review the sources and quality of evidence used in the development of economic decision models in health technology assessments (HTAs). METHODS: All economic decision models developed as part of the NHS Research and Development HTA Programme between 1997 and 2003 were reviewed. Quality of evidence was assessed using a hierarchy of data sources developed for economic analyses. RESULTS: Decision models are parameterized using diverse sources of evidence (e.g. randomized controlled trials, observational studies, expert opinion). Evidence on the main clinical effect was mostly identified and quality assessed as part of the companion systematic review/meta-analysis of the HTA and therefore reported in a transparent and reproducible way. For the other model inputs (i.e. adverse events, baseline clinical data, resource use and utilities), the search strategies for identifying relevant evidence were rarely made explicit and in a number of reports the sources of specific evidence were unclear due to poor reporting. CONCLUSIONS: A more formal and replicable approach to identification and assessment of quality of model inputs is required to reduce the 'black box' nature of decision models, and lead to less scepticism regarding model outputs.
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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 | 0.112 | 0.431 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".