Bibliographic record
Abstract
Basket designs and Multi-Arm Multi-Stage (MAMS) trials enhance efficiency in evaluating multiple treatments across various indications. Chen et al. (2016) proposed a 2-arm 2-stage randomized basket design, testing l=1 treatment vs. control. We expand this to evaluate multiple treatments (l>1) vs. a common control. This multi-arm, 2-stage design is broadly applicable and features rigorous scientific and statistical design. We extended Chen's statistical calculations and used simulations to evaluate type I error rate, power and sample size. We applied the Dunnett test to prune inactive indications in the interim analysis and pool active indications in the final analysis. We compared Chen's 2-arm design with our multi-arm design, validating the accuracy of our simulation approach and effective type I error control. The proposed design maintained stable power with smaller sample sizes compared to single treatment trials, providing a foundation for broader analytical applications. This design offers an efficient method to evaluate multiple therapeutic approaches within a single trial, enhancing the identification of effective treatments across various indications.
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How this classification was reachedexpand
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 | 0.032 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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; both teacher heads agree on what is shown here.
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".