P0831 Improving efficiency in early phase clinical trials for inflammatory bowel disease through use of external control arms
Notice bibliographique
Résumé
Abstract Background Although randomised controlled trials (RCTs) are the gold standard for evaluating medical interventions, enrolling enough patients in early-phase trials in inflammatory bowel disease (IBD) to provide sufficient efficacy and safety information prior to confirmatory trials can be difficult.1,2 In a landscape where combination therapies frequently require monotherapy controls and where placebo responses are well-understood, improving trials with external control (EC) data may enhance phase 1-2 trial designs. Methods Current possibilities of using EC data in IBD trials from both methodological and statistical perspectives were explored. Methods of utilising EC data were reviewed, including results of a systematic review of immune-mediated inflammatory disease (IMID) trials that utilised external control arms (ECAs)3, and an example of Bayesian analyses integrating EC data used in an RCT comparing combination treatment to monotherapy benchmarks.4 How EC data fits into the intersection between operational efficiency and good clinical practice was examined. Results Multiple simultaneous ongoing IBD studies, strict objective clinical endpoints (eg, endoscopic remission), and declining enrolment rates have led to increasing difficulty in meeting IBD trial recruitment goals. The prevalence of add-on and combination therapies suggests that trials may need multiple control arms to fully explore efficacy and safety of investigational treatments. Using EC data can increase power and allow for exploratory comparisons across multiple controls in modest-sized trials. Techniques and considerations on how to ethically and effectively use EC data to improve clinical trials, however, are not as thoroughly discussed in the literature. Systematic review of controlled trial databases for IMID studies utilising ECAs resulted in 18 IBD studies that met the search criteria, over three-fourths of which did not control for baseline characteristics between external and trial data (Table 1).1 Furthermore, half of the quality assessment items during evaluation could not be assigned a rating due to insufficient methodology details provided in publications. Conclusion Multiple factors have led to increasing difficulty for IBD trials reaching their recruitment goals. In early phase trials particularly, the use of EC data may allow studies to increase power and decision-making information. More comprehensive reporting, as well as the creation of a validated instrument for appraising ECA methodology, are required to thoroughly understand and evaluate EC data use in studies. References 1Harris MF, Wichary J, Zadnik M, Reinisch W. Competition for clinical trials in inflammatory bowel diseases. Gastroenterology. 2019;157(6):1457-1461. doi:10.1053/j.gastro.2019.08.020 2Ma C, Solitano V, Danese S, Jairath V. The future of clinical trials in inflammatory bowel disease. Clin Gastroenterol Hepatol. 2024;Jul 16:S1542-3565(24)00635-9. doi:10.1016/j.cgh.2024.06.036 3 ayadi A, Edge R, Parker CE, Macdonald JK, Neustifter B, Chang J, et al. Use of external control arms in immune-mediated inflammatory diseases: a systematic review. BMJ Open. 2023;13(12):e076677. doi:10.1136/bmjopen-2023-076677 4Colombel JF, Ungaro RC, Sands BE, Siegel CA, Wolf DC, Valentine JF, et al. Vedolizumab, adalimumab, and methotrexate combination therapy in Crohn’s disease (EXPLORER). Clin Gastroenterol Hepatol. 2024;22(7):1487-1496. doi:10.1016/j.cgh.2023.09.010
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 | 0,004 | 0,003 |
| 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 | 0,000 |
| 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 ».