Actions for stakeholders to develop better real-world evidence for HTA bodies/payers decision making
Bibliographic record
Abstract
OBJECTIVE: In 2020, RWE4Decisions, a multi-stakeholder initiative commissioned by the Belgian payer, published stakeholder actions to support the generation, analysis, and interpretation of real-world evidence (RWE) to inform the decision making of health technology assessment (HTA) bodies/payers for highly innovative medicines in the European Union (EU). Since 2020, changes in the decision-making environment and advancements in RWE have created an impetus to update stakeholder actions for the EU and Canada. METHODS: RWE4Decisions' experts led focus groups with individual stakeholder groups (HTA bodies/payers, pharmaceutical industry, clinicians, patients, registry holders, and data analytical experts). Each focus group crafted new actions for their stakeholder, then the actions were discussed and revised in a multi-stakeholder meeting, a public webinar, and a public consultation. Themes across actions and meetings were identified. RESULTS: Detailed new actions for each stakeholder group are presented. Key themes identified are the need to address interorganizational fragmentation regarding secondary data use and methodologies to build robust RWE. HTA bodies/payers need to develop a common vision about the potential use of RWE. The role of the whole clinical team as primary data collectors is critical. Opportunities for scientific advice across the life cycle of a medicine are essential, and the implementation of RWE guidance related to HTA is paramount. Progress requires specific, operational actions and a collective effort by a variety of stakeholders. CONCLUSIONS: Carrying out these actions will facilitate the development of methodological best practices for generating RWE to inform HTA of highly innovative medicines and build trust between stakeholders in the use of RWE.
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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 | 0.549 | 0.489 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.008 | 0.049 |
| Research integrity | 0.027 | 0.041 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".