Influence of competing risks on estimates of recurrence risk and breast cancer-specific mortality in analyses of the Early Breast Cancer Trialists Collaborative Group.
Bibliographic record
Abstract
533 Background: Early stage breast cancer is a curable disease with the majority of patients dying of causes other than breast cancer. The influence of these competing risks of death on the interpretation of Kaplan-Meier(KM)-based analyses such as those performed by the Early Breast Cancer Trialists Collaborative Group (EBCTCG) are unknown. Methods: We searched the Clinical Trial Service Unit and Epidemiological Studies Unit website at Oxford University to identify all meta-analyses published by the EBCTCG between 2005 and 2018. Studies were included if they contained KM curves with risk estimates for either breast cancer mortality and/or breast cancer recurrence. The potential influence of competing risks was estimated using a validated multivariate linear model that predicts the amount that the KM risk estimates are biased relative to outcome risk measured with the cumulative incidence function (CIF). Results: The initial search identified 14 analyses published by the EBCTCG with 10 of the 14 studies (71%) susceptible to competing risk bias cited both the number of events of interest and competing events. Eight of the ten studies (80%) had a relative difference between the KM estimate and the competing risk adjusted estimate of more than 10% while 2 of 10 (20%) had a difference of less than 10%. The relative difference between the KM and adjusted estimates was 28.4% for local recurrence, 16.8% for distant recurrence, and 6.7% for breast cancer-specific mortality. There was 2.2% difference between KM and adjusted analyses between 0-4 years and 18.9% beyond 10 years of follow up. Use of KM and CIF-based analysis did not influence treatment effect in the majority of included studies. Conclusions: This study provides estimates for the overestimation of risk in Kaplan-Meier analyses resulting from failure to address competing risk bias. CIFs are more appropriate to measure outcome risk over time and should be used especially for long-term follow-up studies and for analysis of rare events.
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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 | 0.528 | 0.696 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.055 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".