Handling missing birthdates in marginal regression analysis with recurrent events
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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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 | 0.063 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".