Bibliographic record
Abstract
In this presentation some common challenges in survival analysis with large datasets are demonstrated. We investigate the relationship between the age of smoking initiation and some demographic factors in the Canadian Community Health Survey, Cycle 3.1 (CCHS-3.1) dataset. CCHS-3.1 is a large dataset which includes information for over 130000 individuals. We used different techniques for model fitting and model checking. Test-based techniques for the assessment of PH assumption are not very useful as small deviation from the theoretical model leads to the rejection of PH assumption. In contrast graphical approaches seem to be more helpful. However, not every diagnostic graph can be drawn due to large dataset. Preliminary results show that 63% of Canadians ever smoked a whole cigarette. Therefore, it seems more appropriate to use a cure fraction model (Lambert 2007; Stata Journal, 7:(3), pp. 1-25) to handle the large proportion of censored data. However, sampling weights cannot be used in this model. In conclusion, survival analysis for large datasets cannot be done easily. Some challenges include assessment of PH assumption and drawing diagnostic graphs. Besides, use of cure fraction model may not be appropriate if sampling weights cannot be incorporated in the model estimation.
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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 | 0.272 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".