Information about confirmatory studies required for new drugs conditionally approved by Health Canada: A cross-sectional study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,042 | 0,124 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».