Handling missing birthdates in marginal regression analysis with recurrent events
Why this work is in the frame
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Bibliographic record
Abstract
In attempt to provide a practical guide to handling missing birthdate information, this paper examines the strategy proposed by Hu and Rosychuk (2016 Hu, X. J., and R. J. Rosychuk. 2016. Marginal regression analysis of recurrent events with coarsened censoring times. Biometrics 72 (4):1113–22. doi: 10.1111/biom.12503.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) for estimating age-varying effects in a marginal regression analysis of recurrent event times. We conduct empirical studies based on the same dataset that motivated Hu and Rosychuk’s research and explore how analysis outcomes differ when using different distributions for missing birthdates in situations with different sample sizes. Our studies show that Hu and Rosychuk’s assumption of uniformly-distributed birthdates is an appropriate and computationally efficient solution to restricted birthdate information with a reasonably large sample.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it