Joint Analysis of Longitudinal Ordinal Categorical Item Response Data and Survival Times with Cure Fraction
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
For longitudinal ordinal categorical item response data which may not be observable after a subject develops a terminal event, some statistical models have been proposed for the joint analysis of the longitudinal item responses and times to the development of a terminal event denoted as the survival times. All of these models used an accelerated failure time or Cox proportional model for the survival times, which may not be suitable when some of the subjects may be considered as cured and may, therefore, never develop an event. In this article, we propose a new joint model which uses a promotion time cure model for the survival times. Statistical procedures are developed for the inference of the parameters in the model. The proposed model and inference procedures are assessed through a simulation study and application to data from a randomized clinical trial for patients with 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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