Development of a framework on the incorporation of real-world evidence (RWE) into cancer drug funding decisions in Canada: the Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration
Bibliographic record
Abstract
OBJECTIVE: The Canadian Real-world Evidence for Value in Cancer (CanREValue) Collaboration was established in response to growing interest in using real-world evidence (RWE) to support health technology assessment (HTA). CanREValue has developed a framework to generate and use RWE to inform cancer drug funding decisions. DESIGN AND PARTICIPANTS: The RWE framework was developed using a multistage, multistakeholder approach. First, an environmental scan and qualitative study were conducted to understand the current state and key stakeholder perspectives on RWE. Next, five formal working groups (WGs) were established consisting of stakeholders with cancer drug funding expertise including clinicians, patients, methodologists, payers, regulatory decision-makers and data analysts. Through stakeholder consultations, including modified Delphi exercises and workshops, each WG developed specific framework components and identified facilitators and barriers that may impact the uptake of RWE. SETTING: The CanREValue Collaboration consisted of membership and participation from stakeholders and expertise from across Canada. Central research operations were managed from Toronto, Ontario, Canada. OUTCOMES: Development of an RWE framework reflective of the needs and perspectives of stakeholders directly involved and/or impacted by cancer drug funding decisions across Canada. RESULTS: Through an iterative process, a comprehensive RWE framework was developed that outlined the end-to-end processes necessary for the generation and use of RWE for HTA reassessment in Canada. The framework consists of four phases that uses various tools, templates and processes, which can be applied as a whole or in part. A diverse range of stakeholders and expertise is involved in the decision-making of each phase of the process: Phase I: identification, selection and prioritisation of RWE questions; phase II: initiating and planning the RWE study; phase III: conducting the RWE study and phase IV: conducting reassessment. CONCLUSIONS: As the cancer drug funding landscape continues to evolve, the need for RWE to support evidence-based policy reform, pricing and reallocation of funding from low to high value settings is crucial. We have developed a framework that is adaptable and responsive to the changing landscape. The tools, templates and processes within the framework can be applied by various stakeholder groups in whole or in part to support cancer drug funding decision-making in Canada and can be adapted for use in other jurisdictions.
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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 | 0.492 | 0.359 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.025 | 0.060 |
| Scholarly communication | 0.038 | 0.020 |
| Open science | 0.017 | 0.032 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".