A Bayesian semiparametric regression model for current status 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
In survival analysis, interval censoring case I or current status censoring happens if each subject is observed only once for status of occurrence of the event of interest. Current status data often appear along with covariates in cross sectional studies and tumorigenicity studies. Cox’s proportional hazards model has been widely used to explore the relationship between lifetime variable and covariates. In this paper we propose a novel and easy to implement Bayesian approach for analyzing current status data. Under proportional hazards model, baseline survival function and regression parameters are estimated assuming proper prior distributions and implementing Metropolis Hastings algorithm for posterior computation. Methods for both model selection and model validation are suggested. Finite sample performance of the proposed method is evaluated using simulation studies. Intraocular lenses calcification data are analyzed for illustration.
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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 | 0.006 |
| 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