Estimating relative survival for cancer: An analysis of bias introduced by outdated life tables.
Bibliographic record
Abstract
BACKGROUND: Relative survival analyses of cancer data often incorporate outdated information about expected survival when current information is not readily available. The assumption is that any bias introduced into the estimation of expected survival, and hence, into the estimate of relative survival, will be negligible. However, empirical studies of potential bias have yet to be published. DATA AND METHODS: Data are from the Canadian Cancer Registry with mortality follow-up through record linkage to the Canadian Vital Statistics Death Database. Period method relative survival ratios (RSRs) for 2005-2007 were derived using life tables centred on the 2006 Census of Population to estimate expected survival. The analysis was repeated using life tables from 5 and 10 years earlier. RESULTS: Deriving expected survival from life tables 5 years out of date resulted in increases in RSRs for all cancers. These increases became greater with lengthening survival duration. For example, increases in 1-, 5- and 10-year RSRs were 0.2, 0.8 and 1.7 percentage units, respectively, for all cancers combined. Increases in 5-year survival were highest for prostate (2.0) and bladder cancer (1.6); among males (1.2); and among people aged 75 to 99 at diagnosis (1.9). Differences were approximately double when life tables 10 years out of date were used. INTERPRETATION: The use of historical rather than current expected survival data in calculating RSRs for cancer may lead to consequential overestimation of survival.
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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 | 0.279 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".