Regulatory, health technology assessment and company interactions: the current landscape and future ecosystem for drug development, review and reimbursement
Bibliographic record
Abstract
BACKGROUND: Multi-stakeholder interactions have evolved at product and policy levels. There is a need to assess the current and future landscape of interactions between companies, and regulatory and HTA agencies to address challenges and identify areas for improvement. OBJECTIVES: The aims of this study were to review the current interactions within and across regulatory and HTA agencies, and companies' experiences in engaging in these activities; to assess the added value of interactions as well as limitations; to explore the future ecosystem for stakeholder interactions. METHOD: Three separate questionnaires were developed for companies, regulators and HTA agencies, respectively, to assess their experiences and perceptions. The responses were analyzed using descriptive statistics and discussed at a multi-stakeholder workshop. Key outcomes from the surveys and workshop discussion were reported. RESULTS: All seven regulators and seven HTA agencies in the survey indicated that they had stakeholder interactions. More formal collaboration occurred with regulators compared with HTA agencies. All nine companies have taken early advice but indicated the need for future prioritization. Success indicators can be built at the product and therapy levels, with the added value of faster patient access. Four principles were proposed for the future ecosystem: separate remit and functions between regulators and HTA; align processes; converge evidence requirements where possible; increase transparency. CONCLUSIONS: This research brought together regulators, HTA agencies, companies to examine how they interact with one another. We propose measures of value and make recommendations on future evolution to enable better evidence generation and improve regulatory and HTA decision-making.
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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 | 0.207 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.038 | 0.030 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".