Spatial survival analysis: an application to lung cancer data in Manitoba
Bibliographic record
Abstract
Survival data are often collected in cluster such as geographic regions. Incorporating the cluster effect (between cluster dependence and within cluster dispersion) in survival model not only improves the accuracy and efficiency of parameter estimation, but also investigates spatial pattern and identify high-risk areas. The commonly used spatial-survival models are mostly restricted to single-event or competing risks settings, with a few extensions of semi-competing risks setting which only incorporate between cluster variation. This thesis proposed a spatial semi-competing risk model that allows for spatial dependence while estimating the risks of terminal (e.g., death) and non-terminal (e.g., lung cancer) events. A real data application of our model on a merge dataset of Manitoba lung cancer registry and vital statistics was provided to investigate the pattern of events (lung cancer and death) in Manitoba and evaluate the effect of demographic and socio-economic factors on the risk of events. Socio-economic status score, high percentage of visible minority, and high percentage of Indigenous population were found to be risk factors of both lung cancer and death. Male population were at higher risk of both lung cancer and death in comparison to female population. The performance of our proposed model was also evaluated through simulation studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".