PP35 Keeping It Real Around The World: Comparing Real-World Evidence Guidance From Regulatory And Health Technology Assessment Bodies
Bibliographic record
Abstract
Introduction The growing use of real-world evidence (RWE) in pharmaceutical decision-making has prompted various guidelines, including REALISE (REAL World Data In ASia for HEalth Technology Assessment in Reimbursement). We compared RWE guidance from Asian, European, and North American health technology assessment (HTA) and regulatory bodies against the REALISE guidance. Methods Following a previous search for China/Japan guidelines in 2022, websites of the U.S. Food and Drug Administration (FDA), the National Institute for Health and Care Excellence (NICE), the European Medicines Agency (EMA), and the Canadian Agency for Drugs and Technologies in Health (CADTH) were searched for RWE guidance in May 2023. Sections from each guidance were mapped onto REALISE and categorized as “agree,” “mixed,” “disagree,” or “missing” based on coverage/consistency. Results Eleven guidelines were identified: four from Japan, three from China, and one each from FDA, NICE, EMA, and CADTH. No disagreements were found (all mapped sections were tagged “agree”/”mixed”); divergences were in coverage only. Most regulatory guidance had narrower scopes: EMA covered registry-based studies, the FDA’s framework mainly referenced other documents, while Japan had guidance for database and registry data. Conversely, HTA guidelines (CADTH, NICE) were more comprehensive and provided specific recommendations on preferred methods (e.g., transparent reporting) that were “missing” (45 to 46%) from REALISE’s more conceptual discussions. Chinese regulatory guidance was the exception, with similar coverage as REALISE (77% “agree”/“mixed”). Conclusions Guidelines varied in scope, but there was overall concordance where recommendations could be mapped across documents. While regulatory bodies could focus on specific types of RWE, reflecting the specific role of RWE in regulatory evaluations (for example demonstrating safety), guidance for HTA was broader to account for different possible use cases in demonstrating comparative effectiveness and value.
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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 | 0.314 | 0.696 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.052 | 0.050 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".