Confidence interval estimation for the Bland–Altman limits of agreement with multiple observations per individual
Bibliographic record
Abstract
The limits of agreement (LoA) method proposed by Bland and Altman has become a standard for assessing agreement between different methods measuring the same quantity. Virtually, all method comparison studies have reported only point estimates of LoA due largely to the lack of simple confidence interval procedures. In this article, we address confidence interval estimation for LoA when multiple measurements per individual are available. Separate procedures are proposed for situations when the underlying true value of the measured quantity is assumed changing and when it is perceived as stable. A fixed number of replicates per individual is not needed for the procedures to work. As shown by the worked examples, the construction of these confidence intervals requires only quantiles from the standard normal and chi-square distributions. Simulation results show the proposed procedures perform well. A SAS macro implementing the methods is available on the publisher's website.
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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 | 0.153 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".