A Choice of Performance Metrics for Evaluating Predictive Accuracy of Survival Models
Bibliographic record
Abstract
This research critically assessed the predictive accuracy of parametric survival models (Weibull, Exponential, Log-logistic, and Gompertz) against penalized Cox PH models (Ridge, Lasso, and Elastic Net) using both simulated data (sample sizes of 100, 200, and 1000) and real-world data from the Nigerian Demographic and Health Survey (NDHS). The findings showed that parametric models, particularly the Weibull and Log-logistic models, consistently outperformed the others, achieving the highest Concordance Index (C-index) and the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE), indicating superior discrimination and calibration. In contrast, penalized Cox models underperformed, especially with a larger number of covariates, and the Gompertz model exhibited poor predictive performance under all conditions. Notably, parametric models remained stable and consistent even with smaller sample sizes and high-dimensional, complex data. These results highlighted the reliability of parametric models in survival analysis, particularly in small-sample and high-dimensional settings, offering key insights to inform future infant and child health research.
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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 | 0.065 | 0.236 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".