Estimating Net Survival using a Life-Table Approach
Bibliographic record
Abstract
Cancer registries are often interested in estimating net survival (NS), the probability of survival if the cancer under study is the only possible cause of death. Pohar Perme, Stare, and Estéve (2012, Biometrics 68: 113–120) proposed a new estimator of NS based on inverse probability weighting. They demonstrated that existing estimators of NS based on relative survival were biased, whereas the new estimator was unbiased. The new estimator was developed for continuous survival times, yet cancer registries often have only discrete survival times (for example, survival time in completed months or years). Therefore, we propose an approach to estimation for when survival times are discrete. In this article, we describe the stnet command for life-table estimation of NS, adapting the Pohar Perme estimation approach to life-table estimation. Estimates can be made using a period or hybrid approach in addition to the traditional cohort (or complete) approach, and age-standardized survival estimates are available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".