Penalized variable selection with broken adaptive ridge regression for semi-competing risks data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Semi-competing risks data arise when both non-terminal and terminal events are considered in an illness-death model. Such data with multiple events of interest are frequently encountered in medical research and clinical trials. Unlike some recent works on penalised variable selection that deal with the competing risks separately without incorporating possible correlation between them, we perform variable selection in the illness-death model using shared frailty. We propose a broken adaptive ridge (BAR) penalty to encourage sparsity and perform variable selection in an event-specific manner so that the potential risk factors can be selected and their effects can be estimated simultaneously, corresponding to each event in the study. The oracle property of the proposed BAR procedure is established, and its performance is evaluated and compared with other commonly used methods by simulation studies. The proposed method is then applied to the real-life data arising from a colon cancer study.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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