Semi-Parametric Likelihood Inference for Birnbaum–Saunders Frailty Model
Bibliographic record
Abstract
Cluster failure time data are commonly encountered in survival analysis due to different factors such as shared environmental conditions and genetic similarity. In such cases, careful attention needs to be paid to the correlation among subjects within same clusters. In this paper, we study a frailty model based on Birnbaum–Saunders frailty distribution. We approximate the intractable integrals in the likelihood function by the use of Monte Carlo simulations and then use the piecewise constant baseline hazard function within the proportional hazards model in frailty framework. Thereafter, the maximum likelihood estimates are numerically determined. A simulation study is conducted to evaluate the performance of the proposed model and the method of inference. Finally, we apply this model to a real data set to analyze the effect of sublingual nitroglycerin and oral isosorbide dinitrate on angina pectoris of coronary heart disease patients and compare our results with those based on other frailty models considered earlier in the literature.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".