The utility of <scp>real‐world</scp> evidence for benefit‐risk assessment, communication, and evaluation of pharmaceuticals: Case studies
Bibliographic record
Abstract
PURPOSE: In recent years, novel types of real-world evidence (RWE) have played a role in various decision-making processes relating to medicinal products, including regulatory approval, patient access, health technology assessment, safety monitoring, clinical use, and post-approval lifecycle management. We therefore reviewed the potential utility of RWE in the cycle of medicinal product benefit-risk (BR) assessment, communication/risk minimization and evaluation ("BRACE"). METHODS: A convenience sample of illustrative studies was drawn from the published literature and examined. Specifically, we examined the purpose for using RWE, the type of RWE used, its novelty and how it might be integrated with other data and activities of the BRACE cycle, and how it contributed to regulatory decision-making. RESULTS: Eight studies were selected with each illustrating a different activity in the BRACE cycle ranging from BR assessment in the preapproval setting, post-approval assessment of safety or effectiveness, communicating BR information to patients and healthcare professionals, and evaluating the effectiveness of risk minimization initiatives to support a positive BR balance. CONCLUSIONS: RWE has an important role in informing regulatory decision-making regarding the BR management of medicines. With increasing digitalization, facilitating data collection and stakeholder engagement in health, this role is only expected to expand in the future. To reach the full potential of RWE, both regulators and sponsors will need to be familiar with a range of existing and emerging methods for generating and analyzing such evidence appropriately and achieve convergence regarding how different types of RWE can best be used to inform BR management and decision-making.
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 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 | 0.222 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".