Bibliographic record
Abstract
In studies with survival endpoints, it is often of interest to predict the disease risk or survival probabilities in the presence of censored failure times. One commonly used approach is to model the association between the survival outcome and covariates via a semiparametric regression model and use the fitted model for prediction. In this article, we propose two methods to evaluate or predict the survival rates. The first method estimates survival probabilities by matching survival functions, and the second one is based on matching censored quantiles. Unlike traditional regression approaches, the proposed methods directly match the distribution of linear combinations of the covariates to the entire target distribution or parts of it. To accommodate censoring, we adopt a redistribution‐of‐mass technique for the proposed matching censored quantiles. The asymptotic consistency of the resulting estimators is well established. Simulation studies and an example with real data are also provided to further illustrate the practical utilities of our proposals. The proposed methods have been implemented in an R package.
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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 | 0.057 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".