Health Canada Usage of Real World Evidence (RWE) in Regulatory Decision Making compared with FDA/EMA usage based on publicly available information
Bibliographic record
Abstract
PURPOSE: Between January 2020 and December 2021, Health Canada provided a Summary Basis of Decision (SBD) for each of 110 products approved, including 29 oncology products and 21 non-oncology orphan drugs. This review sought to gain insight into how Real Word Evidence (RWE) impacts regulatory decision making. METHODS: SBDs for oncology drugs and non-oncology orphan drugs were reviewed for evidence of use of the RWE or historical data to support regulatory decisions. This information was compared with both FDA and EMA reviews. RESULTS: For the 29 Health Canada-approved oncology products, 11 were approved with Notice of Compliance with Conditions (NOCc) status. Two NOCc approvals received extensive RWE reviews, while two other approvals briefly mentioned the use of RWE/historical data. Of the 12 NOC approvals, one received RWE reviews. FDA also approved all 29 drugs, 14 of which received extensive comments on RWE and/or historical data and 8 of which mentioned RWE or historical data. EMA approved 25 of the 29 products and provided extensive comments on 10. Four products received a mention of RWE review. The percentages of submissions with RWE/historical reviews conducted by Health Canada, FDA and EMA were 24.1, 75.9 and 56.0 respectively. Of the 21 non-oncology orphan drugs, Health Canada provided priority review status to 11, with extensive RWE comments in 5 and the mention of RWE in 2 of the regular approvals. Two approvals that used third-party data were not included in the comparison. FDA approved 19, and provided extensive RWE assessment on 5 and mentioned use of historical data in 8. EMA approved 17 and provided extensive RWE and historical comments in 7 and mentioned historical data in 4. The percentages of submissions with RWE/historical reviews by Health Canada, FDA and EMA were 36.8, 68.4 and 64.7 respectively. CONCLUSIONS: Use of Real World Data is common among FDA/EMA reviews and Health Canada used RWE in recent NOCc and orphan drug approvals.
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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 | 0.343 | 0.730 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.040 | 0.039 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".