Robust prediction of the cumulative incidence function under non‐proportional subdistribution hazards
Bibliographic record
Abstract
Abstract Prediction of a cause‐specific cumulative incidence function (CIF) for data containing competing risks is of primary interest to clinicians when making treatment decisions for patients given their prognostic characteristics. The Fine–Gray regression model is widely used to incorporate multiple prognostic factors, yet it is not applicable when the assumption of proportional subdistribution hazards (PSH) does not hold. In this study we investigate the properties of the partial‐likelihood estimator from the Fine–Gray model under non‐proportionality and propose a robust risk prediction procedure that is not sensitive to the assumption and is more favourable in practice because it bypasses the complicated modelling of time‐varying covariate effects. We evaluate the prediction performance of our procedure in simulations and demonstrate an application in predicting the absolute risk of locoregional recurrence for breast cancer patients, given a set of prognostic factors in which not all of them satisfy the PSH assumption. The Canadian Journal of Statistics 44: 127–141; 2016 © 2016 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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".