Health Canada's use of its priority review process for new drugs: a cohort study
Bibliographic record
Abstract
OBJECTIVES: Priority reviews of new drug applications are resource intensive and drugs approved through this process have a greater likelihood of acquiring a serious safety warning compared to drugs approved through the standard process. Therefore, when Health Canada uses priority reviews, it is important that it accurately identifies products that represent a significant therapeutic advance. The purpose of this study is to compare Health Canada's use of priority reviews to therapeutic ratings from two independent organisations, the Patented Medicine Prices Review Board (PMPRB) and the French drug bulletin Prescrire International, over the period 1 January 1997-31 December 2012. DESIGN: Cohort study. DATA SOURCES: Annual reports of the Therapeutic Products Directorate, and the Biologics and Genetic Therapies Directorate; evaluations of therapeutic innovation from PMPRB and Prescrire International; WHO Collaborating Centre for Drug Statistics Methodology. INTERVENTIONS: Assessments by PMPRB and Prescrire International treated as a gold standard for postmarket therapeutic value. PRIMARY AND SECONDARY OUTCOME MEASURES: Drug-by-drug comparison between the review status from Health Canada and the therapeutic status from PMPRB/Prescrire using κ values, and positive and negative predictive values. Analysis of the per cent of all new drug applications put into the priority review category over the 16-year period. RESULTS: Health Canada approved 426 new drugs, and 345 were evaluated by PMPRB and/or Prescrire. 91 had a priority review and 52 were assessed as innovative (p=0.0003). Agreement between Health Canada and PMPRB/Prescrire was only fair (κ=0.330). The positive predictive value for Health Canada's review assignments was 36.3% and the negative predictive value was 92.5%. CONCLUSIONS: Health Canada's assignment of a priority approval to a new drug submission is only a fair predictor of the drug's therapeutic value once it is marketed. Health Canada should review its criteria for using priority reviews.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".