Detection of Outliers in Bioequivalence Studies Data Analysis with Williams Design
Bibliographic record
Abstract
Background: Drug Regulatory agencies all over the world generally discourage exclusion of outliers in a BE (BE) study; on the other hand in routine bio-statistical work we take these into the account. If the decision rules for identifying the outliers are clearly mentioned before the start of the study and laid down in protocol by the responsible biostatistician in collaboration with clinicians, the problem of outliers can be dealt smartly without jeopardizing the whole study for redoing. The purpose of this article is to introduce procedure for reliably detecting outlier subject(s) with Williams design. Experimental: Literature review reveals many different methods for the detection of outlier values in BE studies; most of them are for BE of two treatments. For BE studies with more than two treatments use of Williams design seems imperative; but inclusion and deletion of outlying subjects may lead to profound effect on conclusion of BE which in turn may be dangerous for the health. The suggested method is an adjustment to a previously introduced method using exploratory data analysis technique such as principle component analysis and Andrews curves.
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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 | 0.120 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".