On the three‐arm non‐inferiority trial including a placebo with a prespecified margin
Bibliographic record
Abstract
Three-arm trials including the experimental treatment, an active reference treatment and a placebo are recommended in the guidelines of the ICH and EMEA/CPMP as a useful approach to the assessment of assay sensitivity. Generally, the acceptable non-inferiority margin Δ has been defined as the maximum clinically irrelevant difference between treatments in many two-arm non-inferiority trials. However, many recent articles discussing three-arm trials have considered a design with unknown Δ which is the prespecified fraction f of unknown effect size of the reference drug, where the prespecified fraction f is treated as if it were a revised margin. Therefore, these methods cannot be applied to the case where the acceptable non-inferiority margin must be a prespecified difference between treatments. In this paper, we propose a statistical test procedure for three-arm non-inferiority trials with the margin Δ defined as a prespecified difference between treatments under the situation that the primary endpoints are normally distributed with a common, but unknown, variance. In addition, we derive the optimal allocation that minimizes the total sample size. The proposed method is illustrated with data on a randomized controlled trial on major depressive disorder.
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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 | 0.089 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".