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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Simple dichotomous updating methods improved the validity of polytomous prediction models
Kirsten Van Hoorde1, Yvonne Vergouwe, Dirk Timmerman
1Department of Electrical Engineering ESAT-SCD, KU Leuven-University of Leuven, Leuven, Belgium.
Journal of Clinical Epidemiology
|July 16, 2013
Summary
Model updating methods for predicting polytomous outcomes, especially those with low prevalence categories, were evaluated. Recalibration improved calibration without harming discrimination, suggesting it as a preferred method.
Area of Science:
- Medical statistics
- Clinical prediction models
- Machine learning in healthcare
Background:
- Prediction models often perform poorly in new settings.
- Polytomous outcome models can suffer from low prevalence categories.
- Model updating is crucial for maintaining performance in new environments.
Purpose of the Study:
- To determine optimal model updating methods for polytomous outcome prediction.
- To evaluate recalibration, revision, and redevelopment strategies.
- To assess impact on model discrimination and calibration.
Main Methods:
- Case studies on testicular and ovarian tumors.
- Sequential dichotomous modeling for polytomous outcomes.
- Validation data split into updating and testing sets.
- Assessment of discrimination (c-statistics) and calibration (observed vs. expected prevalences).
Main Results:
- No updating method improved discrimination of original models.
- Recalibration, revision, and redevelopment significantly improved calibration.
- Redevelopment showed instability regarding overfitting and performance.
Conclusions:
- Simple dichotomous updating methods are effective for polytomous models.
- Recalibration is the preferred method for model updating.
- Revision or redevelopment may be viable with larger validation sets.
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