Nonparametric statistical inference method for partial areas under receiver operating characteristic curves, with application to genomic studies
Bibliographic record
Abstract
Recently ROC50 index-the area under the lower portion of the receiver operating characteristic (ROC) curve up to the first 50 false positives-has been increasingly widely used in genomic research. Unfortunately, statistical inferences on the ROC50 index are not commonly drawn due to a lack of handy statistical inference methods and/or software tools. In this paper, we reviewed developments in statistical methods for the partial areas under ROC curves and using nonparametric methods we derived a simple and direct variance calculation formula for the partial areas, different from existing methods in the literature. We have also verified our method through simulation studies and compared our method with existing bi-normal approaches. We have shown that the partial area has an asymptotic normal distribution using trimmed U-statistics theory. On the basis of this asymptotic normality, we have given formulas for the confidence interval and the test statistic and we reported on their application to a genomic study of sample size approximately 10,000.
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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 | 0.026 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".