Concurrent Prediction of Multiple Survival Outcomes with a Refined Stacking Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Xing et al. (2019) 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 utilizing 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 modelling comorbidity diseases. This research offers a new dimension to MTPS, allowing a diverse array of applications spanning various domains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it