Survival Analysis in the Presence of Competing Risks: The Example of Waitlisted Kidney Transplant Candidates
Bibliographic record
Abstract
Competing events (or risks) preclude the observation of an event of interest or alter the probability of the event's occurrence and are commonly encountered in transplant outcomes research. Transplantation, for example, is a competing event for death on the waiting list because receiving a transplant may significantly decrease the risk of long-term mortality. In a typical analysis of time-to-event data, competing events may be censored or incorporated into composite end points; however, the presence of competing events violates the assumption of "independent censoring," which is the basis of standard survival analysis techniques. The use of composite end points disregards the possibility that competing events may be related to the exposure in a way that is different from the other components of the composite. Using data from the Scientific Registry of Transplant Recipients, this paper reviews the principles of competing risks analysis; outlines approaches for analyzing data with competing events (cause-specific and subdistribution hazards models); compares the estimates obtained from standard survival analysis, which handle competing events as censoring events; discusses the appropriate settings in which each of the two approaches could be used; and contrasts their interpretation.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".