Participant diversity and inclusive trial design: a meta-epidemiologic study of Canadian randomized clinical trials
Notice bibliographique
Résumé
OBJECTIVES: To describe the demographic and social identities of participants in contemporary Canadian randomized clinical trials (RCTs). STUDY DESIGN AND SETTING: A meta-epidemiologic study included published reports of phase 2 and 3 RCTs that exclusively recruited adults living in Canada and were registered on ClinicalTrials.gov between January 1, 2010, and December 31, 2019. Study design and participant demographics were abstracted from eligible articles in duplicate using frameworks for understanding participant diversity such as PROGRESS-PLUS. RESULTS: We identified 118 RCTs with 17,387 participants. Most reported participant sex (n = 105, 89.0%), few reported gender (n = 12, 10.2%), and none reported both. Among articles reporting sex, there were 11,066 female (63.6%), 5402 male (32.8%), and one intersex (<0.1%) participants. There were 477 women (54.1%) and 404 men (45.9%) participants. No studies reported gender diverse participants. When excluding studies that only recruited one sex and/or gender, 51.8% of participants were male (n = 4774/9219) and 47.5% were men (n = 446/850). Race and/or ethnicity was reported for 4124 participants (23.7%) in 31 of 118 (26.3%) of RCTs; of these, 72.0% were White (n = 2969), 2.7% were Black (n = 113), and 0.2% were Indigenous (n = 7). Eligibility criteria related to specific PROGRESS-PLUS factors were rare except for cognition (n = 42, 35.6%), substance use (n = 25, 21.7%), pregnancy (n = 29, 24.5%), breastfeeding (n = 16, 13.6%), and older age (n = 26, 22.0%). CONCLUSION: The data are encouraging regarding representation of female and women participants in Canadian trials. Due to underreporting of other identities, we cannot identify additional groups who may be underrepresented. Work to improve reporting of race and/or ethnicity, among other identities, is needed. PLAIN LANGUAGE SUMMARY: Clinical trials tell us what drugs and procedures are helpful for patients. In certain specialties, like cancer and heart disease, clinical trials are made up mostly of men, White people, and younger people. This means that the results of these trials may be different for other groups of people, especially older people, women, and racialized people, who are more likely to have these diseases. We looked at the demographic identities of all participants in 118 Canadian clinical trials that were done between 2010 and 2019. Of the 17,387 participants, there were 11,066 female, 5402 male, 477 women, 404 men, and one intersex participant. We could find the race and/or ethnicity for only 4124 participants in 31 of the trials. Most participants (72.0%) were White, and only 2.7% were Black and 0.2% were Indigenous. These results tell us that reporting of identities in Canadian clinical trials is incomplete. Canadian clinical trialists should do a better job telling us who is in their trials. These results suggest that Canadian clinical trials are not representative of the general population, and that we need to explore the reasons that people are not participating in clinical trials.
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 | 0,292 | 0,512 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,004 |
| Méta-épidémiologie (sens large) | 0,015 | 0,026 |
| Bibliométrie | 0,007 | 0,013 |
| Études des sciences et des technologies | 0,008 | 0,006 |
| Communication savante | 0,010 | 0,005 |
| Science ouverte | 0,008 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».