Assessment of gender representation in clinical trials leading to FDA approval for oncology therapeutics between 2014 and 2019: A systematic review‐based cohort study
Notice bibliographique
Résumé
BACKGROUND: Ensuring representative data accrual in clinical trials is important to safeguard the generalizability of results and to minimize disparities in care. This study's goal was to evaluate differences in gender representation in trials leading to US Food and Drug Administration (FDA) cancer drug approvals. METHODS: An observational study was conducted from January 2014 to April 2019 using PubMed and the National Institutes of Health trials registry for primary trial reports. The National Cancer Institute's Surveillance, Epidemiology, and End Results program and US Census were consulted for national cancer incidence. The outcome was an enrollment incidence disparity (EID), which was calculated as the difference between male and female trial enrollment and national incidence, with positive values representing male overrepresentation. RESULTS: There were 149 clinical trials with 59,988 participants-60.3% and 39.7% were male and female, respectively-leading to 127 oncology drug approvals. The US incidence rates were 55.4% for men versus 44.6% for women. Gender representation varied by specific tumor type. Most notably, women were underrepresented in thyroid cancer (EID, +27.4%), whereas men were underrepresented in soft tissue cancer (EID, -26.1%). Overall, women were underrepresented when compared with expected incidence (EID, +4.9%; 42% of trials). CONCLUSIONS: For many specific tumor types, women are underrepresented in clinical trials leading to FDA oncology drug approvals. It is critical to better align clinical trial cohort demographics and the populations to which these data will be extrapolated. LAY SUMMARY: This study assesses whether gender disparities exist in clinical trials leading to US Food and Drug Administration (FDA) cancer drug approvals. From January 2014 to April 2019, 149 clinical trials leading to FDA oncology drug approvals showed 60.3% and 39.7% of the enrollees were male and female, respectively. Gender representation varied by specific tumor when compared with the expected incidence rate of cancer in the United States, although women were more often underrepresented. Increased efforts are needed with regard to ensuring equitable representation in oncology clinical trials.
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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 | 0,016 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».