US FDA Advisory Panel Members’ Assessment of Premarket Approval Process and Suggestions for Improvement
Notice bibliographique
Résumé
Importance: The manufacturing and marketing of medical devices is regulated by the US Food and Drug Administration (FDA), and the FDA premarket approval (PMA) process evaluates the safety and effectiveness of medical devices. The PMA process includes a detailed scientific, regulatory and quality system review and is critical to ensure that novel devices are safe, effective, and meet the needs of patients. Objective: To survey current voting members serving on panels of the FDA's Medical Devices Advisory Committee to better characterize panel decision-making and identify steps for improvement. Design, Setting, and Participants: This qualitative survey study included 36 questions that were mailed to FDA device panelists regarding their opinions on the influence of sources of information, pivotal trial design, quality of evidence, panel composition and internal deliberative process, time allocation, and impartiality of the FDA. The survey was mailed to the members of all 18 FDA device panels in January and February 2017. Data were collected from January to May 2017 and analyzed from 2018 to 2019. Exposures: Respondents read and returned the aforementioned paper survey, while nonrespondents did not. Main Outcomes and Measures: The main outcomes included panel members' perceptions, and their implications for process improvement. χ2 or Fisher exact tests were used to test differences between subgroups. Results: Of 64 of 92 panel members who responded (69.6%), 38 of 64 (59.4%) were male, 3 of 63 (4.8%) were Black respondents, 46 of 63 (73.0%) were White respondents, and 36 of 60 (60.0%) were in academic practice. The mean (range) panel service was 6.8 (1-22) years with 3.9 (1-19) meetings attended. Overall, respondents considered information presented by the FDA unbiased, and 28 of 61 (45.9%) believed that pivotal trials were frequently well-designed, 55 of 62 respondents (88.7%) suggested FDA consult panel members preemptively regarding trial design and 54 of 64 (84.4%) regarding the device label. Most indicated that prior FDA approval of another device serving the same medical purpose (43 of 62 [69.4%]) or approval in other countries with comparable regulatory regimes, such as Canada and Europe (39 of 62 [62.9%]), would make them more likely to recommend approval. Respondents rated written information (50 of 60 [83.3%]), live presentations (43 of 58 [74.1%]), and prior professional knowledge (41 of 60 [68.3%]) as the most important sources of information in deciding whether to recommend approval. Additionally, 52 of 58 respondents (89.7%) recommended that a panel member-only executive session would allow more clarity and honesty in deliberations, and 33 of 59 (55.9%) believed a three-fourths majority appropriate for recommending approval, which would be a deviation from the current system in which an overall vote is reported without designation of a vote threshold. Conclusions and Relevance: In this survey study of FDA device panel members, respondents wanted improved study designs, more relevant clinical data, including from other countries, involvement of panelists in study design and device label development, and inclusion of an executive session. Demographically, panels could be made more diverse.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».