Response Adaptive Randomization Using Biomarkers with Exponentially Decreasing Probability Sequence
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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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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".