Prediction accuracy for the cure probabilities in mixture cure models
Bibliographic record
Abstract
Prediction accuracy of a cure model when it is used to predict the cure probability of a patient is an important but not well-addressed issue in survival analysis. We propose a method to assess the prediction accuracy of a mixture cure model in predicting cure probability based on inverse probability of censoring weights to incorporate the censoring and latent cure status in the data. The inverse probability of censoring weight-adjusted estimator is shown to be consistent for the true expected prediction error for cure probability. A simulation study shows that the estimator performs well with finite samples when subjects with censored survival times greater than the largest uncensored time are identified as cured, an approach that is often used in mixture cure model literature to increase model identifiability. The simulation study also investigates the performance of the estimator with different thresholds to identify cured subjects and the estimator based on observed (training) data only. The method is applied to bone barrow transplant data for leukemia patients for assessing prediction accuracy for the cure probabilities.
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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 | 0.029 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".