Biasplot: A Package to Effective Plots to Assess Bias and Precision in Method Comparison Studies
Bibliographic record
Abstract
Bland and Altman's (1986, Lancet 327: 307–310) limits of agreement have been used in many clinical research settings to assess agreement between two methods of measuring a quantitative characteristic. However, when the variances of the measurement errors of the two methods differ, limits of agreement can be misleading. biasplot implements a new statistical methodology that Taffé (Forthcoming, Statistical Methods in Medical Research) recently developed to circumvent this issue and assess bias and precision of the two measurement methods (one is the reference standard, and the other is the new measurement method to be evaluated). biasplot produces three new plots introduced by Taffé: the “bias plot”, “precision plot”, and “comparison plot”. These help the investigator visually evaluate the performance of the new measurement method. In this article, we introduce the user-written command biasplot and present worked examples using simulated data included with the package. Note that the Taffé method assumes there are several measurements from the reference standard and possibly as few as one measurement from the new method for each individual.
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 | 0.057 | 0.236 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.152 | 0.024 |
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".