Concurrent Prediction of Multiple Survival Outcomes with a Refined Stacking Algorithm
Bibliographic record
Abstract
Xing et al. developed prediction algorithms, termed multi-task prediction algorithms using revised stacking (MTPS), to enable us to conduct concurrent prediction for multiple outcome variables with high-dimensional predictors integrated into the prediction process. Their algorithms employed the strategy of the stacking algorithm to construct a multi-task learner through a two-step procedure, where separate single learners are constructed in Step 1, and mutually carried information among those learners is then facilitated in Step 2. While their methods handle both continuous and binary outcomes, as well as a mix of them, they are not applicable to the context of survival data, which arises commonly in applications.Expanding their work to handle the prediction of multiple survival outcomes, we develop a new concurrent prediction algorithm by using the revised residual stacking framework, where the parametric accelerated failure time (AFT) model and Elastic Net AFT model are employed. Through simulation studies and a real-data application, we demonstrate that the novel enhancement of MTPS for survival outcomes surpasses the performance of their single learners. Consequently, this newly refined MTPS is recommended for modeling comorbidity diseases. This research offers a new dimension to MTPS, allowing a diverse array of applications spanning various domains. Supplementary materials for this article are available online.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".