P0831 Improving efficiency in early phase clinical trials for inflammatory bowel disease through use of external control arms
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.244 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".