The maze of real-world evidence frameworks: from a desert to a jungle! An environmental scan and comparison across regulatory and health technology assessment agencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aim: Regulatory and health technology assessment (HTA) agencies have increasingly published frameworks, guidelines, and recommendations for the use of real-world evidence (RWE) in healthcare decision-making. Variations in the scope and content of these documents, with updates running in parallel, may create challenges for their implementation especially during the market authorization and reimbursement phases of a medicine's life cycle. This environmental scan aimed to comprehensively identify and summarize the guidance documents for RWE developed by most well-established regulatory and reimbursement agencies, as well as other organizations focused on healthcare decision-making, and present their similarities and differences. Methods: RWE guidance documents, including white papers from regulatory and HTA agencies, were reviewed in March 2024. Data on scope and recommendations from each body were extracted by two reviewers and similarities and differences were summarized across four topics: study planning, choosing fit-for-purpose data, study conduct, and reporting. Post-authorization or non-pharmacological guidance was excluded. Results: Forty-six documents were identified across multiple agencies; US FDA produced the most RWE-related guidance. All agencies addressed specific and often similar methodological issues related to study design, data fitness-for-purpose, reliability, and reproducibility, although inconsistency in terminologies on these topics was noted. Two HTA bodies (National Institute for Health and Care Excellence [NICE] and Canada's Drug Agency) each centralized all related RWE guidance under a unified framework. RWE quality tools and checklists were not consistently named and some differences in preferences were noted. European Medicines Agency, NICE, Haute Autorité de Santé, and the Institute for Quality and Efficiency in Health Care included specific recommendations on the use of analytical approaches to address RWE complexities and increase trust in its findings. Conclusion: Similarities in agencies' expectations on RWE studies design, quality elements, and reporting will facilitate evidence generation strategy and activities for manufacturers facing multiple, including global, regulatory and reimbursement submissions and re-submissions. A strong preference by decision-making bodies for local real-world data generation may hinder opportunities for data sharing and outputs from international federated data networks. Closer collaboration between decision-making agencies towards a harmonized RWE roadmap, which can be centrally preserved in a living mode, will provide manufacturers and researchers clarity on minimum acceptance requirements and expectations, especially as novel methodologies for RWE generation are rapidly emerging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it