On the Implications of Ignoring Competing Risk in Survival Analysis: The Case of the Product-Limit Estimator
Bibliographic record
Abstract
Although the Kaplan-Meier (KM) is a single event model, it is frequently used in literature with datasets that are assumed to be cause-specific without any proper verification. It is crucial to evaluate the implication of this on the probability estimates. This study compares the estimates of the cumulative incidence functions to the complement of the product-limit estimator (1-KM). The KM was found to inflate probability estimates when the dataset is unverified for competing risk. Estimates with lower standard errors and a larger area under the Receiver Operation Characteristic (ROC) curve were related to datasets verified for competing events, while estimates with datasets unverified for competing risk had lower area under the ROC curve and higher standard errors. The results support the idea that since the product-limit estimator is a cause-specific model, it naturally performs better for a single event model than in the case of several events; hence, it is necessary to verify the dataset for competing events. The findings of this study clearly suggest that before choosing a modelling strategy, one should confirm competing risks in the survival dataset.
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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 | 0.245 | 0.583 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".