Oncologic Drugs Advisory Committee Recommendations and Approval of Cancer Drugs by the US Food and Drug Administration
Notice bibliographique
Résumé
IMPORTANCE: The US Food and Drug Administration (FDA) advisory committees influence decisions relating to the regulatory approval of drugs in the United States. Outside of the field of oncology, prosponsor voting bias has been observed among members with financial conflicts of interest (FCOIs). OBJECTIVE: To explore factors associated with Oncologic Drugs Advisory Committee (ODAC) recommendations and the influence ODAC members' FCOIs on the drug approval process. DESIGN, SETTING, AND PARTICIPANTS: Retrospective analysis of 82 ODAC meeting transcripts between January 2000 and December 2014. Analysis was restricted to meetings at which votes were cast relating to oncologic drugs. The influence of methodology of trials supporting approval and frequency and type of self-reported FCOIs of voting members was explored using logistic regression. MAIN OUTCOMES AND MEASURES: ODAC recommendation for drug approval and subsequent FDA approval. RESULTS: Eighty-two transcripts of ODAC meetings between January 2000 and December 2014 were available for analysis. During the time period analyzed, ODAC members voted on 68 applications in 79 meetings (the remaining 3 meetings included voting questions regarding postmarketing safety or trial design). There was agreement between ODAC recommendations and final FDA approval; FDA approval was received for all 41 drugs that ODAC recommended approval. Additionally, the FDA approved 7 out of 41 agents that were not recommended for approval by ODAC (κ = 0.83). In 51 of 79 meetings, more than 1 trial was available to support the indication of a particular drug, and favorable ODAC recommendations were more likely when this was the case (odds ratio [OR], 1.82; 95% CI, 1.19-2.78; P = .01). Availability of randomized data did not appear to be important with selected single-arm phase 2 trials leading to recommendations for approval, especially in rare diseases. There has been a significant reduction in FCOIs over time (31 of 77 voting members [40%] in 2000 vs 0 of 20 voting members in 2014 [0%]; P < .001). Recommendations for approval were made in 28 of 47 meetings with members reporting FCOIs while among meetings with no reported FCOIs, recommendations for approval were made in 13 of 35 meetings (OR, 1.19; 95% CI, 0.97-1.46; P = .10). No significant association between ODAC recommendations and FDA approval was observed for members with FCOIs with the sponsor (OR, 1.79; 95% CI, 0.97-1.46; P = .19 and OR, 3.48; 95% CI, 0.84-14.35; P = .09, respectively) compared with members with FCOIs with competitors (OR, 1.06; 95% CI, 0.78-1.44; P = .72 and OR, 0.94; 95% CI, 0.69-1.28; P = .69, respectively). CONCLUSIONS AND RELEVANCE: Availability of multiple trials is associated with higher odds of ODAC recommendation and drug approval. Availability of randomized data appears less important. Declaration of FCOIs among ODAC members was frequent during the time period of interest but has decreased significantly over time. There is no apparent association between FCOIs and ODAC recommendations and subsequent FDA approval.
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 | 0,084 | 0,363 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».