An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables
Bibliographic record
Abstract
Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our approach uses a flexible modeling framework where the effects of continuous biomarkers are represented using free knot B-splines. We then estimate key parameters by marginalizing over all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination for efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider both pre-categorized and continuous biomarkers, the latter potentially having complex, nonlinear relationships to the outcome and treatment effect. Through simulations, we derive the operating characteristics of our design and compare its performance to existing methods.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".