A250 POTENTIAL OF ADAPTIVE DESIGNS TO ADDRESS CURRENT CHALLENGES IN TRIAL EVALUATION OF TREATMENTS FOR CROHN’S DISEASE
Bibliographic record
Abstract
Several characteristics of Crohn’s disease, such as varying phenotypes and inconsistent outcome measures can make it a difficult disease to study. Further, in the setting of clinical trials, investigators often face challenges pertaining to poor study recruitment, prohibitive costs, and high placebo rates. To discuss the potential of adaptive clinical trials in Crohn’s disease trials. Adaptive trials are a relatively new approach to clinical trials that allow for modification of the trial design based on interim analyses of data collected during the study. There are a number of different adaptive designs. First, response adaptive randomization refers to changes in the randomization ratio based on an interim analysis. Second, sample size reassessment allows for changes in allocation of treatment ratio to favor drugs that show positive results. Third, seamless studies, a type of design that allows for immediate continuation from one phase of a clinical trial to another, can be utilized to combine Phase II and Phase III trials. Finally, adaptive enrichment allows for modification of eligibility criteria based on interim analyses that suggest one subgroup has a more favorable response. Master protocols involve a single overarching protocol that allows investigators to evaluate multiple treatments in more than one patient group or disease type. Statistical analyses within adaptive trials can be complex, and the use of Bayesian methods is often ideal. Bayesian statistics is a statistical approach whereby a probability expresses a degree of belief in an event, established from the combination of prior data and data accumulated throughout the trial. We will review various adaptive designs and their potential in Crohn’s disease clinical trials. Master protocols and adaptive designs may improve the quality and efficiency of future trials in Crohn’s disease. None
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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 | 0.229 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".