Real-World Evidence to Reinforce Clinical Trial Evidence in Health Technology Assessment: A Critical Review of Real-World Evidence Requirements from Seven Countries and Recommendations to Improve Acceptance
Bibliographic record
Abstract
BACKGROUND: Real-world evidence (RWE) can reinforce clinical trial evidence in health technology assessment (HTA). OBJECTIVES: Review HTA bodies' (HTAbs) requirements for RWE, real uses, and acceptance across seven countries (Brazil, Canada, France, Germany, Italy, Spain, and the United Kingdom) and outline recommendations that may improve acceptance of RWE in efficacy/effectiveness assessments and appraisals processes. METHODS: RWE requirements were summarized based on HTAbs' guidelines. Acceptance by HTAbs was evaluated based on industry experience and case studies. RESULTS: As of June 2022, RWE methodological guidelines were in place in three of the seven countries. HTAbs typically requested analyses based on local data sources, but the preferred study design and data sources differed. HTAbs had individual submission, assessment, and appraisal processes; some allowed early meetings for the protocol and/or results validation, though few involved external experts or medical societies to provide input to assessment and appraisal. The extent of submission, assessment, and appraisal requirements did not necessarily reflect the degree of acceptance. CONCLUSION: All the countries reviewed face common challenges regarding the use of RWE. Our proposals address the need to facilitate collaboration and communication with industry and regulatory agencies and the need for specific guidelines describing RWE design and criteria of acceptance throughout the assessment and appraisal processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.121 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".