Semiparametric inference for survival models with step process covariates
Bibliographic record
Abstract
Abstract The authors consider Bayesian methods for fitting three semiparametric survival models, incorporating time‐dependent covariates that are step functions. In particular, these are models due to Cox [Cox ( 1972 )Journal of the Royal Statistical Society, Series B, 34, 187–208], Prentice & Kalbfleisch and Cox & Oakes [Cox & Oakes ( 1984 )Analysis of Survival Data, Chapman and Hall, London]. The model due to Prentice & Kalbfleisch [Prentice & Kalbfleisch ( 1979 )Biometrics, 35, 25–39], which has seen very limited use, is given particular consideration. The prior for the baseline distribution in each model is taken to be a mixture of Polya trees and posterior inference is obtained through standard Markov chain Monte Carlo methods. They demonstrate the implementation and comparison of these three models on the celebrated Stanford heart transplant data and the study of the timing of cerebral edema diagnosis during emergency room treatment of diabetic ketoacidosis in children. An important feature of their overall discussion is the comparison of semi‐parametric families, and ultimate criterion based selection of a family within the context of a given data set. The Canadian Journal of Statistics 37: 60–79; © 2009 Statistical Society of Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".