Professor Endrenyi’s Legacy: An Evaluation of the Regulatory Requirement “Fixed Effects, Rather Than Random Effects, Should Be Used for All Terms”
Bibliographic record
Abstract
PURPOSE: In the latest revision of the guideline for evaluation of bioequivalence (BE), European regulators introduced the requirement for using subjects as fixed factors in the underlying statistical models, even in replicate and semi-replicate studies. The implication was that estimates of within-subject variability were derived with a linear model rather than with a mixed model based on restricted maximum likelihood (REML). While REML-based methods are generally thought to give rise to less biased estimates of variance components, there have been no studies that compared the quality of REML-based estimates and estimates derived via linear models. METHODS: A publication by Endrenyi and Tothfalusi from 1999 described simulations in a fashion that is useful for testing the European Medicines Agency's (EMA) requirement. This study defines 7 scenarios within which 10,000 individual 2-sequence, 2-treatment, 4-period trials are simulated and makes a comparison of the quality of estimates. RESULTS: It is concluded that estimates based on REML are closer to the true values than estimates based on linear models, but significant differences are only shown in two of the seven scenarios tested. REML-based estimators have less variability. Both types of estimates appear negatively biased and will therefore decrease the width of the acceptance range.
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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 | 0.189 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.026 |
| 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".