Variable Selection Methods Based on Pseudo-observations in Competing Risks Analysis
Bibliographic record
Abstract
Competing risks analysis based on the pseudo-observations has been applied to medical studies in recent years. The analysis allows the direct evaluation of the effect of covariates on the cause-specific cumulative incidence function (CIF) using an estimating equation. In a case with a large the number of covariates, variable selection (selecting the covariates that affect the outcome variables) is critical. However, there are few studies addressing the problem of variable selection for the pseudo-observations technique. In this study, we propose two variable selection methods based on pseudo-observations. One is a method based on a criterion derived by an estimator of an expected pseudo quasi-likelihood. The other is a penalized estimating equation, inducing a sparse estimates for model parameters. The estimator given by the penalized estimating equation has so-called oracle properties under some appropriate penalty functions. When applying such a penalization method, it is essential to choose an optimal tuning parameter that determines the magnitude of the penalty. Then, we construct a BIC-type criterion for the ordinary penalized least squares and show it can consistently identify the true set of covariates as the sample size grows. Simulation studies show the performance of the proposed two variable selection methods.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".