Extended biasplot command to assess bias, precision, and agreement in method comparison studies
Bibliographic record
Abstract
Recently, a new statistical methodology to assess the bias and precision of a new measurement method, which circumvents the deficiencies of the Bland and Altman (1986, Lancet 327: 307–310) limits of agreement method, was developed by Taffé (2018, Statistical Methods in Medical Research 27: 1650–1660). Later, the methodology was extended to assess the agreement. In addition, to allow for inferences, simultaneous confidence bands around the bias, precision, and agreement lines were developed (Taffé, 2020, Statistical Methods in Medical Research 29: 778–796). The goal of this article is to introduce the extended biasplot command, which implements these latest developments, and to illustrate its use by applying it to simulated data included with the command. Note that the Taffé method assumes that there are several measurements by one of the two measurement methods and possibly as few as one measurement by the other for each individual. The repeated measurements need not come from the reference standard but from any of the two measurement methods. This is a great advantage because it may sometimes be more feasible to gather repeated measurements either with the reference standard or the new measurement method.
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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 | 0.107 | 0.292 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.012 |
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".