Considerations for Developing a Reassessment Process: Report from the Canadian Real-World Evidence for Value of Cancer Drugs (CanREValue) Collaboration’s Reassessment and Uptake Working Group
Bibliographic record
Abstract
The Canadian Real-world Evidence for Value in Cancer Drugs (CanREValue) Collaboration was established to develop a framework for generating and using real-world evidence (RWE) to inform the reassessment of cancer drugs following initial health technology assessment (HTA). The Reassessment and Uptake Working Group (RWG) is one of the five established CanREValue Working Groups. The RWG aims to develop considerations for incorporating RWE for HTA reassessment and strategies for using RWE to reassess drug funding decisions. Between February 2018 and December 2019, the RWG attended four teleconferences (with follow-up surveys) and two in-person meetings to discuss recommendations for the development of a reassessment process and potential barriers and facilitators. Modified Delphi methods were used to gather input. A draft report of recommendations (to December 2018) was shared for public consultation (December 2019 to January 2020). Initial considerations for developing a reassessment process were proposed. Specifically, reassessment can be initiated by diverse stakeholders, including decision makers from public drug plans or industry stakeholders. The reassessment process should be modelled after existing deliberation and recommendation frameworks used by HTA agencies. Proposed reassessment outcome categories include maintaining status quo, revisiting funding criteria, renegotiating price, or disinvesting. Overall, these initial considerations will serve as the basis for future advancements by the Collaboration.
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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 | 0.641 | 0.715 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.014 | 0.025 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".