Pre‐study analytical method validation: comparison of four alternative approaches based on quality‐level estimation and tolerance intervals
Bibliographic record
Abstract
Abstract In industry and in laboratories, it is crucial to continuously control the validity of the analytical methods used to follow the products' quality characteristics. Validity must be assessed at two levels. The ‘pre‐study’ validation aims at demonstrating before use that the method will be able to achieve its objectives. The ‘in‐study’ validation is intended to verify, by inserting quality control (QC) samples in routine runs, that the method remains valid over time. At these two levels, the analytical method will be claimed valid if it is possible to prove that a sufficient proportion of analytical results is expected to lie within given acceptance limits [−λ,λ] around the nominal value. This paper presents and compares four approaches to checking the validity of a measurement method at the pre‐study level. They can be classified into two categories. In the first, a lower confidence bound for the estimated probability π of a result lying within the acceptance limits is computed and compared with a given acceptance level. Maximum likelihood and delta methods are used to estimate the quality level π and the corresponding estimator variance. Two approaches are then proposed to derive the confidence bound: the asymptotic maximum likelihood approach and a method proposed by Mee (Commun. Stat. Theory Methods 1988; 17(5):1465–1479). The second category of approaches checks whether a tolerance interval for hypothetical future measurements lies within the predefined acceptance limits [−λ,λ]. β‐expectation and β–γ‐content tolerance intervals are investigated and compared in this context. These four approaches are illustrated on a bioanalytical HPLC‐UV analytical process and compared through simulations. Copyright © 2008 John Wiley & Sons, Ltd.
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 | 0.117 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".