Comparison of Outlier Detection Methods in Crossover Design Bioequivalence Studies
Bibliographic record
Abstract
The significance of bioequivalence (BE) studies is rising due to large scale production and utilization of generic products all over the world. The correct identification of outlying data in BE studies is substantial for deciding two products either bioequivalence or bioinequivalent. For the detection of outliers in BE studies with the crossover designs different methods have been suggested in the literature. In the present work, we compared three outlier detection tests; (i) the Likelihood distance (LD) test (ii) the estimated distance (ED) test and the principal component analysis (PCA) test. In this work, the PCA test has been first time compared with the LD and ED test. For the purpose of comparison, we used two-way and three-way BE crossover data sets on linear and logarithmic scales. During the course of work it was found interesting and note-worthy that the performances of the ED and PCA tests in the sense of outlier detection are better than the LD test and this performance persists even for the log-transformed data. The results of our simulation study also indicated that the performance of the ED test for outliers’ identification is better than the other two tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".