A three‐stage clinical trial design for rare disorders
Bibliographic record
Abstract
Many clinical trials of uncommon diseases are underpowered because of the difficulty of recruiting adequate numbers of subjects. We propose a clinical trial design with improved statistical power compared to the traditional randomized trial for use in clinical trials of rare diseases. The three-stage clinical trial design consists of an initial randomized placebo-controlled stage, a randomized withdrawal stage for subjects who responded, and a third randomized stage for placebo non-responders who subsequently respond to treatment. Test level and power were assessed by computer-intensive exact calculations. The three-stage clinical trial design was found to be consistently superior to the traditional randomized trial design in all cases examined, with sample sizes typically reduced by 20 per cent to 30 per cent while maintaining comparable power. When a treatment clearly superior to placebo was considered, our design reached a power of 75 per cent with a sample of 21 patients compared with the 52 needed to attain this power when only a randomized controlled trial was used. In situations where patient numbers are limited, a three-stage clinical trial design may be a more powerful design than the traditional randomized trial for detecting clinical benefits.
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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 | 0.153 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".