Semiparametric accelerated failure time model for length-biased data with application to dementia study
Why this work is in the frame
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Bibliographic record
Abstract
A semiparametric accelerated failure time (AFT) model is proposed to evaluate the effects of risk factors on the unbiased failure times for the target population given the observed length-biased data. The analysis of length-biased data is complicated by informative right censoring due to the biased sampling mechanism, and consequently the techniques for conventional survival analysis are not applicable. We propose estimating equation methods for estimation and show the asymptotic properties of the proposed estimators. The small sample performance of the estimating methods are investigated and compared with that of existing methods under various underlying distributions and censoring mechanisms. We apply the proposed model and estimating methods to a prevalent cohort study, the Canadian Study of Health and Aging (CSHA), to evaluate the survival duration according to diagnosis of subtype of dementia.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it