Analysis of indirect treatment comparisons in national health technology assessments and requirements for industry submissions
Bibliographic record
Abstract
Aim: To determine the preferred methodologies of health technology assessment (HTA) agencies across Europe, Canada and Australia to ascertain acceptance of indirect treatment comparisons (ITC) as a source of comparative evidence. Method: A review of official submission guidelines and analysis of comments in HTA submissions that have used different ITC methodologies. Conclusion: ITC is generally accepted as a technique that allows demonstration of noninferiority to a comparator provided the chosen methodology and underlying assumptions are clear and justified. However, HTA agencies are more likely to closely scrutinize submitted data and evaluate statistical significance of results when superiority is claimed. In addition, the HTA agencies in scope tended to be cautious and only accept ITC data as support for similarity of treatments.
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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 | 0.805 | 0.948 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".