Joint Analysis of Longitudinal Proportional Measurements and Survival Times Based on Generalized Mean‐Variance Mixed Model and Cox Proportional Hazards Model
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Bibliographic record
Abstract
ABSTRACT Longitudinal proportional data, which are restricted in a closed interval, are frequently observed together with survival data in clinical trials and other medical studies. In this paper, we propose a new model for the joint analysis of longitudinal proportional and survival data. This model uses generalized mean‐variance mixed model for longitudinal outcomes, which avoids parametric assumption for their distribution, and Cox proportional hazards model for survival times. A procedure is developed to estimate the parameters in the proposed model based on quasi‐likelihood for longitudinal data and partial likelihood for survival data with a Laplace approximation for the joint likelihood. A random weighting method is proposed to calculate the variance of these parameter estimators. The performance of the proposed model and estimation procedures are assessed through simulation studies and the application to the analysis of data from a randomized clinical trial on early breast cancer.
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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) | 0.001 | 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