Variable selection for competing risk regression models: recommendations for analyzing data from epidemiological studies
Bibliographic record
Abstract
Abstract When fitting competing risks regression models, a variety of variable selection methods exist, including backward selection on the subdistribution hazard, on the cause-specific hazards, and penalized methods. However, a benchmark study comparing these different procedures is lacking. We conducted an extensive simulation study to compare three variable selection procedures in terms of both model selection ability and predictive accuracy. 5120 datasets were simulated in various conditions aiming at being representative of real applications in clinical epidemiology. Results show that the performance of backward selection procedure can be affected by implementation choices. Even for scenarios with a high numbers of events per variable (EPV), the true model is rarely identified by any of the selection procedures. Survival predictions were assessed with time-dependent AUC and show similar performances for all methods. We also provided an application on stem cell transplanted patients in hematology. We concluded that the identification of the true model in competing risk regression is a very difficult task, and suggest some recommendations to analysts: (1) to report event per variable for the event type of interest and (2) to use multiple methods to deal with model uncertainty and avoid implementation pitfalls.
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How this classification was reachedexpand
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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".