Transferability of real-world data across borders for regulatory and health technology assessment decision-making
Bibliographic record
Abstract
Recently, there has been increased consideration of real-world data (RWD) and real-world evidence (RWE) in regulatory and health technology assessment (HTA) decision-making. Due to challenges in identifying high-quality and relevant RWD sources, researchers and regulatory/HTA bodies may turn to RWD generated in locales outside of the locale of interest (referred to as "transferring RWD"). We therefore performed a review of stakeholder guidance as well as selected case studies to identify themes for researchers to consider when transferring RWD from one jurisdiction to another. Our review highlighted that there is limited consensus on defining decision-grade, transferred RWD; certain stakeholders have issued relevant guidance, but the recommendations are high-level and additional effort is needed to generate comprehensive guidance. Additionally, the case studies revealed that RWD transferability has not been a consistent concern for regulatory/HTA bodies and that more focus has been put on the evaluation of internal validity. To help develop transferability best practices (alongside internal validity best practices), we suggest that researchers address the following considerations in their justification for transferring RWD: treatment pathways, nature of the healthcare system, incidence/prevalence of indication, and patient demographics. We also recommend that RWD transferability should garner more attention as the use of imported RWD could open doors to high-quality data sources and potentially reduce methodological issues that often arise in the use of local RWD; we thus hope this review provides a foundation for further dialogue around the suitability and utility of transferred RWD in the regulatory/HTA decision-making space.
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.
How this classification was reachedexpand
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 | 0.041 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".