Evaluation of some properties of individual bioequivalence (IBE) from replicate-design studies
Bibliographic record
Abstract
BACKGROUND: One of the claimed benefits of the individual bioequivalence (IBE) approach has been that the aggregate regulatory model rewards a test formulation when it has a within-subject variation smaller than the reference product. Hauck et al. [1996] demonstrated that, in the absence of random variations, this property of IBE was due to the tradeoff between the difference of the means and the deviation between the intrasubject variances of the two formulations. The tradeoff was a consequence of the aggregate regulatory model. However, calculations of Endrenyi and Hao [1998] showed that, in the presence of random variations, not only rewards but also penalties can arise due to chance alone. METHODS: A data set of 55 investigations made public by the FDA in 1999 and containing replicate crossover designs was analyzed. Two parameters, AUC and Cmax, were determined in each investigation. RESULTS: The analyses of the FDA data indicate that: rewards and penalties occur at similar frequencies, large rewards and penalties are recorded quite often, and the aggregate IBE model is rather insensitive to the difference between the estimated means and is compatible with the frequent occurrence of large deviations. CONCLUSION: Rewards and penalties, apparently arising from random variations, can affect regulatory decisions on the acceptance of IBE and can lead to incorrect conclusions.
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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 | 0.261 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".