OP48 Interactions Between Regulatory, Health Technology Assessment And Companies: Multi-Stakeholder Survey On The Current Experiences And Future Landscape Evolvement
Bibliographic record
Abstract
Introduction The interactions between regulators, health technology assessment (HTA), and companies play a significant role in the process of getting medicine to patients. These have evolved at a product level as well as at a policy and cross-jurisdictional level; however, it is important these activities are adding value for stakeholders involved. A survey conducted in March 2021 assessed the current interactions from multi-stakeholders, and their perceptions on the added value these interactions bring to better decision-making. Methods Three separate questionnaires containing nine questions were developed to assess the perceptions from pharmaceutical companies, regulators, and HTA agencies. The three questionnaires contained analogous questions where appropriate. The company questionnaire was sent to senior management at 19 international pharmaceutical companies, the agency survey was sent to 32 agencies (17 regulatory agencies and 15 HTA agencies) in Australia, Canada, Europe, and Asia. Results Seven regulators, seven HTA agencies, and nine companies responded to the survey. All regulators and HTAs indicated they have interactions with their peer agencies, as well as between regulators and HTA. The top areas of interactions for regulators were formal work-sharing between regulators during review (86% response) and regulatory strengthening (86%), whilst for HTAs, interactions between HTA on methodology/framework (83%) and HTA capacity building (67%). Regulatory-HTA interactions were seen to have fewer practical benefits, which may suggest areas for improvement. Both companies and agencies believed an effective engagement model should support evidence generation; agencies also viewed an aligned process and improved decision-making as important. Respondents believed that an ideal ecosystem for interactions should facilitate separate remits for stakeholders, converged requirements, aligned process and increased transparency and trust. Conclusions This survey provided a snapshot of the current landscape interactions between stakeholders during the life cycle of new medicines, identified the areas where value is added and improvement are needed. Suggested building blocks to improve future interactions included early scientific advice, alignment of evidence requirements, and a collaborative approach among all stakeholders.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".