Empirical likelihood confidence regions for the evaluation of continuous‐scale diagnostic tests in the presence of verification bias
Bibliographic record
Abstract
Abstract In a continuous‐scale diagnostic test, when a cut‐off level is given, the performance of the test in distinguishing diseased subjects from non‐diseased subjects can be evaluated by its sensitivity and specificity. Joint inferences for sensitivity and specificity as well as cut‐off level play an important role in the assessment of the diagnostic accuracy of the test. Most current studies on this topic focus on complete data cases. However, in some studies, only a portion of subjects given their screening test results ultimately have their true disease status verified. In addition, the verification may depend on the test result and the subject's observed characteristics. Directly applying full data methods to verified subjects results in biased estimates, known as verification bias. In this paper, based on a general framework that combines empirical likelihood and general estimation equations with nuisance parameters, we propose various bias‐corrected joint empirical likelihood confidence regions for sensitivity and specificity with verification‐biased data. Thorough simulation studies are conducted to compare the finite sample performance of the proposed confidence regions in terms of coverage probabilities, and some suggestions are provided accordingly. Finally, an example is provided to illustrate the proposed methods.The Canadian Journal of Statistics41: 398–420; 2013 © 2013 Statistical Society of Canada
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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 | 0.127 | 0.583 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".