Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: evidence from a Canadian population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Cause-specific (CS) and net survival in a relative survival framework (RS) are two of the most common methods for estimating cancer survival. In this paper, we assess the differences in results produced by two permutations of cause-specific and relative survival applied to estimating cancer survival and disparities in cancer survival, using data from First Nations and non-Aboriginal populations in Canada. METHODS: Subjects were members of the 1991 Canadian Census Mortality Cohort, a population-based cohort of adult respondents to the 1991 Long Form Census who have been followed up for incident cancers and death through linkage to administrative databases. We compared four methods: relative survival analyses with ethnicity-specific life tables (RS-ELT); relative survival with general population life tables (RS-GLT); cause-specific survival with a broad definition of cancer death (CS-Broad); and cause-specific survival with a narrow definition of cause of death (CS-Narrow) and applied these to the nine most common cancers among First Nations. RESULTS: Apart from breast and prostate cancers, RS-ELT, RS-GLT, and CS-Broad tended to produce similar estimates of age-standardized five-year survival, whereas CS-Narrow yielded higher estimates of survival. CS-Narrow estimates were particularly unlike those based on the other methods for cancers of the digestive and respiratory tracts. Estimates of disparities in survival were generally comparable across the four methods except for breast and prostate cancers. CONCLUSIONS: Cancer surveillance efforts in sub-populations defined by race, ethnicity, geography, socioeconomic status, or similar factors are necessary for identifying disparities and monitoring progress toward reducing them. In the absence of routine monitoring of cancer survival and cancer survival disparities in these populations, estimates generated by different methods will inevitably be compared over time and across populations. In this study, we demonstrate that caution should be exercised in making these comparisons, particularly in interpreting cause-specific survival rates with an unknown or narrow definition of cancer death and in estimates of breast and prostate cancer survival and/or disparities in survival generated by different methods.
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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 | 0.106 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".