Efficient semiparametric mixture inferences on cure rate models for competing risks
Bibliographic record
Abstract
Abstract Cancer patients may die from causes other than the diagnosed cancer. In a study of patients treated for soft tissue sarcoma, the patients may die from the disease or die without experiencing disease recurrence. In addition, a substantial proportion of the patients will remain cancer‐free after surgical resection of the tumour, and therefore will not be at increased risk of any type of failure. Our goal is to describe the effect of adjuvant chemotherapy simultaneously on the probabilities of long‐term survival, death from cancer, or death from other causes. To this end, we propose a semiparametric mixture model to determine the effects of factors on the probability of occurrence, allowing the surviving fraction, and the hazard rate conditional on each of the failure types. These quantities are combined in the mixture approach using a multinomial logistic model and a class of semiparametric transformation models. Estimation of the regression and nonparametric parameters is achieved with a novel nonparametric maximum likelihood approach. Statistical inferences can be conveniently made from the inverse of the observed information matrix. Simulation studies show that the procedures work well in practical settings. The methodology is illustrated with data from the soft tissue sarcoma study. The Canadian Journal of Statistics 43: 420–435; 2015 © 2015 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 | 0.074 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".