Adaptive clinical trial designs to detect interaction between treatment and a dichotomous biomarker
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Biomarkers play a crucial role in the design and analysis of clinical trials for personalized medicine. One major goal of these trials is to derive an optimal treatment scheme based on each patient's biomarker level. Although completely randomized trials may be employed, a more efficient design can be attained when patients are adaptively allocated to different treatments throughout the trial using biomarker information. Therefore, we propose a new adaptive allocation method based on using multiple regression models to study treatment–biomarker interactions. We show that this perspective simplifies the derivation of optimal allocations. Moreover, when implemented in real clinical trials, our method can consolidate all the covariates which may not be related to the treatment–biomarker interaction for a joint analysis. Our general idea can be applied to diverse models to derive optimal allocations. Simulation results show that both the optimal allocation and the proposed design can lead to a more efficient trial. The Canadian Journal of Statistics 41: 525–539; 2013 © 2013 Statistical Society of Canada
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it