Progression and Death as Competing Risks in Ovarian Cancer
Bibliographic record
Abstract
Background: Progression of a cancer disease and dying without progression can be understood as competing risks. The Cause-Specific Hazards Model and the Fine and Gray model on cumulative incidences are common statistical models to handle this problem. The pseudo value approach by Andersen and Klein is also able to cope with competing risks. It is still unclear which model suits best in which situation. Methods: For a simulated dataset and a real data example of ovarian cancer patients who are exposed to progression and death the three models are examined. We compare the three models with regards to interpretation and modeling requirements. Results: In this study, the parameter estimates for the competing risks are similar from the Cause-Specific Hazards Model and the Fine and Gray model. The pseudo value approach yields divergent results which are heavily dependent on modeling details. Conclusions: The investigated approaches do not exclude each other but moreover complement one another. The pseudo value approach is an alternative that circumvents proportionality assumptions. As in all survival analyses, situations with low event rates should be interpreted carefully.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.354 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".