Competing risk analysis in a large cardiovascular clinical trial: An <scp>APEX</scp> substudy
Bibliographic record
Abstract
Competing risk methods are time-to-event analyses that account for fatal and/or nonfatal events that may potentially alter or prevent a subject from experiencing the primary endpoint. Competing risk methods may provide a more accurate and less biased estimate of the incidence of an outcome but are rarely applied in cardiology trials. APEX investigated the efficacy of extended-duration betrixaban versus standard-duration enoxaparin to prevent a composite of symptomatic deep-vein thrombosis (proximal or distal), nonfatal pulmonary embolism, or venous thromboembolism (VTE)-related death in acute medically ill patients (n = 7513). The aim of the current analysis was to determine the efficacy of betrixaban vs standard-duration enoxaparin accounting for non-VTE-related deaths using the Fine and Gray method for competing risks. The proportion of non-VTE-related death was similar in both the betrixaban (133, 3.6%) and enoxaparin (136, 3.7%) arms, P = .85. Both the traditional Kaplan-Meier method and the Fine and Gray method accounting for non-VTE-related death as a competing risk showed equal reduction of VTE events when comparing betrixaban to enoxaparin (HR/SHR = 0.65, 95% 0.42-0.99, P = 0.046). Due to the similar proportion of non-VTE-related deaths in both treatment arms and the use of a univariate model, the Fine and Gray method provided identical results to the traditional Cox model. Using the Fine and Gray method in addition to the traditional Cox proportional hazards method can indicate whether the presence of a competing risk, which is dependent of the outcome, altered the risk estimate.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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 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".