A gradient boosting decision tree based estimation method for the mixture cure model
Why this work is in the frame
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Bibliographic record
Abstract
Cure models are useful tools for analyzing censored survival data with a cured fraction. However, existing semiparametric estimation methods still rely on restrictive parametric assumptions, and existing nonparametric estimation methods only work with single covariates. In this work, we propose a gradient boosting decision tree based method to estimate the mixture cure model. The new method inherits the features of the original gradient boosting decision tree method and provides more accurate estimates of the cure probability and the relative risk for uncured subjects than existing methods when there are no a priori parametric assumptions on the forms of complex covariate effects in the model. This is demonstrated with small mean square errors in the estimates of the cure probability, relative risk score, and survival function in a simulation study with large samples. The method also has the potential to deal with high-dimensional covariates. The proposed method is illustrated with a large sample study of colon cancer.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it