Quality of evidence considered by Health Canada in granting full market authorisation to new drugs with a conditional approval: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: This study examines the characteristics of studies that Health Canada uses to grant full marketing authorisation for products given a conditional approval between 1 January 1998 and 30 June 2017. DESIGN: Cohort study. DATA SOURCES: Journal articles listing drugs that fulfilled their conditions and received full marketing authorisation, Notice of Compliance database, Notice of Compliance with conditions website, Qualifying Notices listing required confirmatory studies, clinicaltrials.gov, PubMed, Embase, companies making products being analysed, journal articles resulting from confirmatory studies. INTERVENTIONS: None. PRIMARY AND SECONDARY OUTCOME MEASURES: Characteristics of studies-study design (randomised controlled trials, observational), primary outcome used (clinical, surrogate), blinding, number of patients in studies, patient median age, number of men and women. RESULTS: Eleven companies confirmed 36 publications for 19 products (21 indications). Twenty-nine out of the 36 studies were randomised controlled trials (RCTs) but only 10 stated if they were blinded. Twenty used surrogate outcomes. The median age of patients was 56 (IQR 44-61). The median number of men per study/trial was 184 (IQR 58-514) versus women 141 (IQR 46-263). CONCLUSIONS: Postmarket studies required by Health Canada had more rigorous methodology than those required by either the Food and Drug Administration or the European Medicines Agency. There were still deficiencies in these studies. The absence of blinding in the majority of RCTs may introduce bias in their results. The use of surrogate outcomes especially in oncology trials means that improvements in survival are not available. The relatively young age of patients, even for products for cancer, means that predicting how the elderly will respond is often unknown. The almost universal finding that men outnumbered women may make it hard to differentiate responses by sex. These results raise potential concerns about the quality of evidence that Health Canada accepts.
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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 | 0.162 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".