Effect of NONMEM minimization status and number of replicates on bootstrap parameter distributions for population pharmacokinetic models: A case study
Bibliographic record
Abstract
Aims Bootstrap (BS) parameter distributions are often used to characterize estimation uncertainty and determine confidence intervals (CI) for population pharmacokinetic (PPK) model parameters. These results are used to guide inferences about clinical relevance of covariate effects and other model components. The goal of this work was to compare BS parameter distributions using a published PPK model for oxaprozin (OX) under different minimization and re-sampling conditions. Methods Nonparametric BS analyses with NONMEM were conducted on a PPK model for OX and resulting parameter distributions were summarized by: 1) number of BS replicates (REPS), and 2) minimization (MIN) and $COVARIANCE (COV) status. Results For those runs reporting parameter estimates, BS CI for all parameters 1) did not change by more than 9% after 1000 BS REPS; 2) were unaffected by MIN status (<5% change), and most CI were unaffected by COV status (<5% change in all but 1 parameter). Conclusions The number BS REPS should be investigated for each problem, but a general estimate of 1000 REPS may be a useful starting point. MIN status did not affect BS CI for this case. Clinical Pharmacology & Therapeutics (2005) 77, P2–P2; doi: 10.1016/j.clpt.2004.11.010
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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 | 0.161 | 0.414 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".