Destructive cure rate models under proportional odds and associated likelihood inference
Bibliographic record
Abstract
In this work, we introduce a flexible destructive cure rate model for lifetime data. We assume the number of competing causes of the event of interest to follow the Weighted Poisson distribution (including length-biased Poisson, negative binomial, and exponentially weighted Poisson [EWP]) and the lifetimes of the noncured individuals to follow a proportional odds survival model. The baseline odds distribution is considered to be either Weibull or log-logistic distribution. A damage distribution is introduced due to the fact that some of the competing causes may not remain active following treatment. The statistical inference is developed for this model under right-censored data. The maximum likelihood estimation of the model parameters is then developed with the full usage of expectation–maximization method. Model discrimination between destructive negative binomial cure rate model and destructive EWP cure rate model is carried out by likelihood-based criteria through AIC and BIC. An extensive simulation study is performed to evaluate the performance of all the models and inferential methods developed here. A real data example on cutaneous melanoma is analyzed for illustrative purpose.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".