Impact of Regulatory Approval Status on CADTH Reimbursement of Oncology Drugs and Role of Real-World Evidence on Conditional Approvals from 2019 to 2021
Bibliographic record
Abstract
Real-world evidence (RWE) is health and outcomes data generated from a patient's journey through the health care system or disease process (i.e., real-world data). RWE is now having an increasingly important role in regulatory/reimbursement decisions. This article examines reimbursement recommendations by the Canadian Agency for Drugs and Technology in Health (CADTH) on oncology drugs approved between 2019 and 2021. Oncology drugs with a Summary Basis of Decision (SBD) for original marketing approvals were used to generate a corresponding list of CADTH final clinical recommendations for review. Of the 45 oncology drugs approved by Health Canada, CADTH granted positive funding recommendations to all 11 drugs that had priority review approvals. Two of the 17 drugs with standard reviews did not file to CADTH and 3 received a negative recommendation. Of the 17 drugs with Notice of Compliance with Conditions (NOCc) status, three were not filed to CADTH and four were under active reviews. Of the ten completed NOCc reviews, all contained RWE from sponsors and six received a negative decision on their first review. No significant differences in review times were found between the three approval statuses. Regulatory approval status appeared to influence reimbursement outcomes in Canada and evaluation of 10 NOCc approvals provided little insight regarding robustness of RWE required for more favorable considerations.
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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 | 0.305 | 0.753 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".