PP23 Lost In Translation? The Differences In The Use Of Real-World Evidence Across Key Markets
Bibliographic record
Abstract
Introduction Health Technology Assessment (HTA) agencies have recognized the importance of real-world evidence (RWE) to inform access decision-making and different HTA agencies establish distinct requirements for their local jurisdictions. The objective of this study is to understand the differences of RWE included in HTA reports and HTA agencies’ perception of RWE. Methods HTA reports from agencies in France, Germany, Spain, Italy, United Kingdom (UK), Canada, Australia and South Korea from January 2011 to November 2021, including original submissions, resubmissions, extensions of original indications and renewals were analyzed. Results Across the eight countries, RWE has been used in nineteen percent of all HTA reports (N=2,960/15,561), with an exponential increase observed between 2019 and 2021. RWE on clinical effectiveness was mostly used in HTA submissions in the UK (twenty-two percent), with twenty-six percent perceived with full acceptance. In contrast, RWE on safety and epidemiology was reported widely in HTA reports in France and Germany (83% and 87%), respectively. Ninety-three percent of RWE received full acceptance in France, followed by forty-four percent in Germany. A mixed picture of the types of RWE included in HTA reports was observed in the other countries, with high variance of acceptance (between 5 to 37%). Conclusions France, Germany, and the UK are the top three countries with a large proportion of HTA reports where RWE was mentioned. The type of RWE used is related to a large extent to the local evidence requirements. For example, RWE around epidemiology was included widely in Germany due to the needs of providing local data for budget impact analyses required by the Federal Joint Committee (G-BA); RWE on tolerability as reported in periodic safety update reports (PSURs) needs to be included in French HTA submissions. RWE on clinical effectiveness has been evaluated the most by the UK HTA bodies.
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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 | 0.300 | 0.657 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".