Joint survival and longitudinal modelling for combined cohort data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A primary goal of survival analysis is modelling the time from an initial event to a failure event and the factors which affect the hazard rate. In various applications, the observed data can consist of a combination of right-censored failure times and left-truncated right-censored failure times by merging the data collected from incident and prevalent cohort studies with follow-up, respectively. Furthermore, in addition to the observed failure/censoring times, the survival data typically includes time-invariant covariates as well as longitudinal measurements collected throughout the failure/censoring time durations. We introduce three novel estimation methods for a joint proportional hazards and longitudinal model where incident and prevalent cohort data are combined. Using simulated data, we compare the performance of the combined cohort estimation procedures and use these techniques to model the relationship between the mortality of mature female baboons based on their longitudinally measured glucocorticoid hormone levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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