Addressing uncertainty in relative effectiveness assessments by HTA organizations
Bibliographic record
Abstract
This study outlines the ways in which different health technology assessment (HTA) organizations deal with uncertainty in relative effectiveness assessments (REAs), using the GRADE framework as a common reference. Guidelines regarding REA and uncertainty assessment methods and three most recent HTA reports (as of April 2020) of seven HTA organizations in Germany, England and Wales, France, the Netherlands, Europe (EUnetHTA), the USA, and Canada were included. First, it was analyzed how each organization addressed uncertainty on the following levels of evidence: (i) individual studies, (ii) body of evidence for one outcome, (iii) body of evidence across all outcomes, and (iv) added net benefit. Second, the extent to which HTA organizations considered the eight domains of certainty of evidence defined by GRADE was assessed. For individual studies, checklists were the most common approach to express uncertainty (4/7 organizations). Uncertainty in the body of evidence for all outcomes and in added benefit was combined in a single conclusion by five organizations. All organizations reported on at least 4/5 downgrading domains of GRADE, while the three upgrading domains were reported less. The operationalization of the assessment of multiple domains was unclear due to vague or absent guidelines. HTA organizations consider most domains of the GRADE framework, but approaches to assess uncertainty within REAs on different levels of evidence differ substantially between organizations. More alignment and guidance on the best methods to deal with uncertainty within HTA could lead to more clarity for stakeholders and to more aligned reimbursement recommendations.
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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 | 0.684 | 0.819 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| 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; 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".