Response Adaptive Randomization Using Biomarkers with Exponentially Decreasing Probability Sequence
Why this work is in the frame
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Bibliographic record
Abstract
In this article, it is proposed to study the application of Response Adaptive Randomization (RAR) design in clinical trials. The approach involves the prediction of treatment outcomes based on the biomarker of patients using a regression model. The focus is on rare diseases to efficiently allot the patients among various treatments so as to ensure not only the clinical rights but also the maximum possible benefits to the patients even when they are in clinical trials. Initially, the method uses conventional equal randomization to understand how well every treatment works in patients and this initial duration is known as burn-in period. The proposed work allocates patients to treatments by using an exponentially decreasing probability sequence instead of the existing linearly decreasing sequence to have higher allocation probability to the efficient treatment. In the case of rare disease, it is observed from simulation study that the use of exponentially decreasing probability sequence in RAR design increases the benefit to the patients in the clinical trials when compared to the existing method that uses linearly decreasing sequence. The study also investigates the performance of the proposed RAR design when used with different regression methods under various scenarios. The performance of the proposed design is measured by the proportion of patients assigned to the best treatment in addition to Type I error and power. From the impressive results, it is suggested that the proposed RAR design can be implemented practically in clinical trials of rare diseases without any apprehension.
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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 | 0.051 | 0.217 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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