A New Margin Function for Anti-infective Trials
Bibliographic record
Abstract
In diverse contexts comparison of groups is giving frequently. Particularly, comparison of groups based on non-inferiority statistical tests is becoming more frequent and have had a very special boom in clinical trials, especially in trials related to testing new anti-infective products. Non-inferiority tests are statistical procedures that allow verify whether a sample provides sufficient evidence that the efficacy of a new treatment is not substantially inferior to the known efficacy of a standard treatment. For the selection of the non-inferiority margin for anti-infective trials, the Food and Drug Administration (FDA) and the Committee for Proprietary of Medical Products (CPMP) have provided some general guidance. In this investigation we propose a new parametric family of margin functions for testing non-inferiority in the context of anti-infective trials. One important feature of this parametric family is that fit together recommendations of FDA and CPMP jointly with some other important mathematical properties underlined in this research.
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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 | 0.010 | 0.305 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".