Threshold-free Measure for Assessing the Performance of Risk Prediction with Censored Data
Bibliographic record
Abstract
The area under the receiver operating characteristic curve (AUC) is a popular threshold-free metric to retrospectively measure the discriminatory performance of medical tests. In risk prediction or medical screening, main interests often focus on accurately predicting the future risk of an event of interest or prospectively stratifying individuals into risk categories. Thus, AUC might not be optimal in assessing the predictive performance for such purposes. Alternative accuracy measures have been proposed, such as the positive predictive value (PPV). Yuan et al. (2015) proposed a threshold-free metric, the average positive predictive value (AP), which is the area under the PPV versus true positive fraction (TPF) curve, when the outcome is binary disease status. In this thesis, we propose the time-dependent AP when the outcome is censored event time. Empirical estimates of the time-dependent AP (AP_t0) are developed, where the inverse weighted probability technique is applied to deal with censoring. In addition, inference procedures — using bootstrap and perturbation resampling—are proposed to construct confidence intervals. We conduct simulation studies to investigate the performance of the proposed estimation and inference procedures in finite samples. The method is also illustrated through a real data analysis.
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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 | 0.037 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".