Information about confirmatory studies required for new drugs conditionally approved by Health Canada: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Health Canada conditionally approves new drugs using its Notice of Compliance with conditions (NOC/c) policy. Under this policy Qualifying Notices (QNs) list confirmatory studies that need to be conducted to confirm the drug's efficacy. This study examines the depth of information about methodology and patient demographics in the confirmatory studies. It also compares the outcomes (surrogate or clinical) used to approve the drugs with the outcomes proposed in the confirmatory studies. METHODS: A list of drugs approved under the NOC/c policy and their QNs were sourced from two previous publications as well as Health Canada's NOC/c website. Patient demographics and study methodology in the confirmatory studies listed in the QNs was recorded and counted. The primary outcome used to approve new drugs was recorded from Health Canada's Summary Basis of Decision website and compared to the type of outcome for studies mentioned in the QNs. RESULTS: Seventy-eight drugs were approved using a NOC/c from the time the first drug was approved under the program in July 1998 until May 18, 2022. QNs were missing or all information was redacted for 3 drugs, the remaining 75 QNs listed 154 studies (median of 2 studies per QN, interquartile range 1,3). The outcome, randomization and blinding could not be determined for any study in 43 (57.3%), 36 (48.0%) and 42 (56.0%) QNs, respectively. No study gave the distribution of men and women and the number of patients was given in 23 (14.9%) studies. The expected time of completion of the studies was available for 36 (23.4%) out of 154 and information to identify studies was present for 77 (50.0%), absent for 23 (14.9%) and unclear for 26 (16.9%). Surrogate outcomes were used to approve 54 (84.4%) of 64 drugs. Eight (14.8%) confirmatory studies for these 54 drugs used clinical outcomes, 15 (27.8%) used surrogate outcomes and outcomes were unknown for 31 (57.4%). Specifically for oncology drugs, 44 were approved with surrogate outcomes and one with a clinical outcome. Eight (18.2%) of the 44 oncology drugs approved with surrogate outcomes had confirmatory studies that used clinical outcomes, 14 (31.8%) used surrogate outcomes and the outcome could not be determined for 22 (50.0%). The sole oncology drug approved with a clinical outcome had a confirmatory study with a surrogate outcome. DISCUSSION: QNs contain little information about the methodology or patient demographics of confirmatory studies. Confirmatory studies with surrogate outcomes were used almost one-third of the time to validate efficacy in drugs initially approved using surrogate outcomes. Health Canada needs to develop a template about what information regarding confirmatory studies should be contained in a QN and rethink its use of confirmatory studies using surrogate outcomes. All the data were gathered by a single individual possibly introducing unintended biases.
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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 | 0.042 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".