Use of real-world evidence in cancer drug funding decisions in Canada: a qualitative study of stakeholders’ perspectives
Bibliographic record
Abstract
BACKGROUND: Real-world evidence (RWE) can provide postmarket data to inform whether funded cancer drugs yield expected outcomes and value for money, but it is unclear how to incorporate RWE into Canadian cancer drug funding decisions. As part of the Canadian Real-World Evidence Value for Cancer Drugs (CanREValue) Collaboration, this study aimed to explore stakeholder perspectives on the current state of RWE in Canada to inform a Canadian framework for use of RWE in cancer drug funding decisions. METHODS: This was a qualitative descriptive study. Qualitative semistructured interviews were conducted from April to July 2018. Participants were Canadian and international stakeholders who had experience with RWE and drug funding decision-making. Thematic analysis was used to analyze data. RESULTS: Thirty stakeholders participated in the study. Five themes were identified. Stakeholders indicated that RWE had value in cancer drug funding decisions. However, a cultural shift is needed to adopt RWE in decision-making. Further, the Canadian infrastructure for real-world data is currently inadequate for decision-making, and there is a need for committed investment in building capacity to collect and analyze RWE. Finally, there is a need for increased collaboration among key stakeholders. INTERPRETATION: The findings of this study suggest that if RWE is to be used in drug funding decisions, there is a need for a cultural shift, improved data infrastructure, committed investment in capacity building and increased stakeholder collaboration. Together with local stakeholder engagement, application of these findings may contribute to optimizing implementation of RWE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".