Assessment of biomarkers for risk prediction with nested case–control studies
Bibliographic record
Abstract
BACKGROUND: Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools. PURPOSE: In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case-control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated. METHODS: To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses' Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers. RESULTS: Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis. CONCLUSIONS: Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.
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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 | 0.010 | 0.020 |
| 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 | 0.000 |
| 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".