Participation of Patients From Racial and Ethnic Minority Groups in Phase 1 Early Cancer Drug Development Trials in the US, 2000-2018
Notice bibliographique
Résumé
Importance: Despite federal initiatives encouraging the enrollment of individuals from racial and ethnic minority groups in US clinical trials, no studies to date have specifically examined demographic disparities among participants in phase 1 drug development trials for patients with metastatic cancer. Objective: To assess trends in the enrollment of patients from racial and ethnic minority groups in US phase 1 therapeutic drug trials for metastatic cancer from 2000 to 2018. Design, Setting, and Participants: In this cross-sectional study, ClinicalTrials.gov was queried in July 2021 to identify completed phase 1 drug trials for metastatic cancer in the US from January 1, 2000, to December 31, 2018, with published results, yielding 221 phase 1 trials with 8309 participants aged 18 years or older with metastatic solid tumors. Proportions of each racial and ethnic group of trial participants were compared with that from the North American Association of Central Cancer Registries' Cancer in North America (CiNA) database. Statistical analysis was performed from July 12, 2021, to March 15, 2022. Main Outcomes and Measures: For each racial and ethnic group, the difference between trial and CiNA proportions was examined using a 2-sample test for equality of proportions with continuity correction. Results: The 8309 phase 1 trial participants (4198 men [50.5%]; median age, 59 years) included 23 American Indian or Alaska Native participants (0.3%), 371 Asian or Pacific Islander participants (4.5%), 514 Black participants (6.2%), 401 of 5076 Hispanic or Latinx participants (7.9%), and 7154 White participants (86.1%). Industry funded 165 of the 221 trials (74.7%). White patients were overrepresented overall compared with the corresponding CiNA cohort (7154 of 8309 [86.1%] vs 4 113 096 of 4 891 486 [84.1%]; difference, 2.0 percentage points; P < .001). There was an increase in overrepresentation of White patients from 2000 to 2011 (trials, 2780 of 3245 [85.7%]; CiNA, 2 378 019 of 2 800 711 [84.9%]; difference, 0.8 percentage points; P = .23) to 2012-2018 (trials, 4374 of 5063 [86.4%]; CiNA, 1 735 077 of 2 090 775 [82.9%]; difference, 3.5 percentage points; P < .001) and corresponding worsening representation of American Indian or Alaska Native patients (2000-2011: trials, 10 of 3245 [0.3%]; CiNA, 10 905 of 2 800 711 [0.4%]; difference, -0.08 percentage points; 2012-2018: trials, 13 of 5063 [0.3%]; CiNA, 9484 of 2 090 775 [0.5%]; difference, -0.20 percentage points), Asian or Pacific Islander patients (2000-2011: trials, 121 of 3245 [3.7%]; CiNA, 75 033 of 2 800 711 [2.7%]; difference, 1.1 percentage points; 2012-2018: trials, 151 of 5063 [3.0%]; CiNA 70 535 of 2 090 775 [3.4%]; difference, -0.75 percentage points), Black patients (2000-2011: trials, 244 of 3245 [7.5%]; CiNA, 322 701 of 2 800 711 [11.5%]; difference, -4.0 percentage points; 2012-2018: trials, 270 of 5063 [5.3%]; CiNA, 255 625 of 2 090 775 [12.2%]; difference, -6.9 percentage points), and Hispanic or Latinx patients (2000-2011: trials, 161 of 1792 [9.0%]; CiNA, 169 297 of 2 800 711 [6.0%]; difference, 3.0 percentage points; 2012-2018: trials, 240 of 3295 [7.3%]; CiNA, 156 118 of 2 090 775 [7.5%]; difference, -0.2 percentage points). Similar disparities were observed when comparing industry-funded and academic center-sponsored trials. Conclusions and Relevance: In this cross-sectional study of participants in phase 1 clinical trials of drugs for metastatic cancer, worsening disparities were observed over time in the accrual of patients from racial and ethnic minority groups. These findings may represent widening inequalities in access to trial sites and worsening systemic biases. More efforts are needed to diversify phase 1 cancer drug trials to improve equity in access to new treatments and to ensure that safety and efficacy findings from early drug trials are generalizable across populations.
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 | 0,010 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».