Numerical methods for the evaluation of individual bioequivalence criteria
Bibliographic record
Abstract
The evaluation of individual bioequivalence (IBE) by bootstrap resampling using common statistical software, for example SAS, is extremely time consuming. In this article, an estimation procedure that can be implemented in a high level language with the same degree of accuracy as SAS is described. The necessary parameter estimating equations under both least square (LSE) and restricted maximum likelihood (REML) methods are given. The algorithms used to numerically compute these values are outlined and tested, in FORTRAN, on several simulated data sets and shown to reproduce SAS results with at least 10(-3) precision. More importantly, the REML bootstrap algorithm reduces the time taken in SAS by a factor of 20. Secondary results indicate that LSE and REML parameter estimates are similar for mild unbalancedness. PROC MIXED, with unstructured (UN) and compound symmetry heterogeneous (CSH) variance structures give the same results except when the subject-by-treatment interaction variance, sigma(2)(D), is 0 in which case CSH significantly overestimates sigma(2)(D) and underestimates the within-treatment variances. It is concluded that bootstrap evaluation of IBE is efficiently done using either the LSE or REML algorithm in FORTRAN.
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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 | 0.035 | 0.285 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads 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".