LASSO-Based Survival Prediction Modeling with Multiply Imputed Data: A Case Study in Tuberculosis Mortality Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Utilizing health administrative datasets for developing prediction models is always challenging due to missing values in key predictors. Multiple imputation has been recommended to deal with missing predictor values. However, predicting survival outcomes using regularized regression, e.g., Cox-LASSO, faces limitations as these methods are incompatible with pooling model outputs from multiple imputed data using Rubin’s rule. In this study, we explored the performance of three statistical methods in developing prediction models with Cox-LASSO on multiply imputed data: prediction average, performance average, and stacked. We considered two hyperparameter selection techniques: minimum-lambda that gives the minimum cross-validated prediction error and 1SE-lambda that selects more parsimonious models. We also conducted plasmode simulations with varying the events per parameter. The stacked approach provided the most robust predictions in our case study of predicting tuberculosis mortality and simulations, producing a time-dependent c-statistic of 0.93 and a well-calibrated calibration plot. The 1SE-lambda technique resulted in underfitting of the models in most scenarios, both in case study and simulation. Our findings advocate the stacked method with minimum-lambda as an effective technique for combining LASSO-based prediction outputs from multiply imputed data. We shared reproducible R codes for future researchers to facilitate the adoption of these methodologies in their research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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