An improved biomarker-guided adaptive patient enrichment design for oncology trials
Bibliographic record
Abstract
The use of biomarkers to guide adaptive enrichment designs in oncology trials presents a promising strategy for increasing trial efficiency and improving the chance of identifying efficacious treatment in the right population. With a well-defined biomarker, such designs can enhance study power and reduce costs by adapting the trial focus to promising populations. However, existing adaptive enrichment designs may not have sufficiently flexible interim decision-making rules, testing procedures, and sample size re-estimation, limiting their full potential. In this research, we propose an improved biomarker-guided adaptive enrichment design that supports dynamic interim decision-making based on treatment effects observed in biomarker-positive, biomarker-negative, and overall populations. The design includes options for early stopping for efficacy or futility in both biomarker-positive and overall populations and incorporates sample size re-estimation using an improved conditional power method to optimize study power. Simulation results show that the proposed design maintains strong control of type I error and delivers high statistical power, with a high probability of correct interim decisions in cases where treatment is effective in either the biomarker-positive or overall population. This novel framework provides a more flexible and efficient approach to conducting oncology trials with heterogenous populations, ensuring that the most appropriate patient populations are selected as the trial progresses.
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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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".