Relative impact characteristic curve: a graphical tool to visualize and quantify the clinical utility and population-level consequences of implementing markers
Bibliographic record
Abstract
PURPOSE: Receiver operating characteristic (ROC) curve analysis is a popular method for evaluating the performance of (bio)markers. However, the standard ROC curve does not directly connect marker performance to patient-related outcomes. Our aim was to fill this gap by proposing a conceptually similar graphical tool that carries information about the clinical uitility of markers. METHODS: We propose a novel graphical tool, the relative impact characteristic (RIC) curve, that depicts the trade-off between the population-level impact of treatment as a function of the size of the treated population for a given marker positivity rule (e.g., a threshold). We establish analogies between the ROC and the RIC curves around the interpretations of shape, slopes, and area under the curve and discuss parametric inference on RIC. RESULTS: As a case study, we used data from a clinical trial on preventive therapy for exacerbations of chronic obstructive pulmonary disease. We illustrate how the RIC curve can be constructed for a predication score and be interpreted in terms of a marker's ability toward concentrating treatment benefit in the population. We discuss hoe the RIC curve can be used to identify a threshold on the risk score based on the maximal acceptable number-needed-to-treat. CONCLUSIONS: The RIC curve enables evaluation of markers in terms of their treatment-related clinical utility. Its analogies with the standard ROC analysis can facilitate its interpretation, bringing a population-based perspective to the activities of diverse marker development and evaluation teams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".