Cure rate quantile regression accommodating both finite and infinite survival times
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
Abstract In survival analysis a proportion of patients may be cured by the treatment, and thus they become risk‐free of the event of interest and their survival times change to infinity. The existence of such a survival fraction often makes the underlying population more heterogeneous and heavily right‐skewed. Compared with the traditional mean‐ or hazard‐based regression methods, quantile regression is more suitable for such survival data as it is more robust against outliers or infinite survival times. Moreover, it offers a comprehensive assessment of the covariate effects on the survival times at different quantile levels. We propose a new cure rate quantile regression model for the entire population including both finite and infinite survival times. By invoking non‐parametric functional estimation an iterative algorithm is developed to estimate the cure rate parameters. The scheme of redistribution‐of‐mass to the right for censored data is adopted to estimate the quantile regression parameters. The consistency and asymptotic normality of the proposed estimators are established. Extensive simulation studies are conducted to evaluate the finite‐sample performance of the proposed method, which is further illustrated with a phase III melanoma clinical trial study. The Canadian Journal of Statistics 45: 29–43; 2017 © 2016 Statistical Society of Canada
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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