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An R-Based Landscape Validation of a Competing Risk Model
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A new concordance measure for risk prediction models in external validation settings
David van Klaveren1, Mithat Gönen2, Ewout W Steyerberg1
1Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands.
Statistics in Medicine
|June 3, 2016
Summary
New concordance measures, model-based concordance (mbc) and c-mbc, quantify case-mix influence on risk model discrimination and are robust to censoring. These measures offer improved interpretation during external validation.
Area of Science:
- Biostatistics
- Epidemiology
- Health Services Research
Background:
- Concordance measures assess risk prediction model discriminative ability.
- Interpreting concordance at external validation is challenging due to differing case-mix.
- Existing measures are sensitive to censoring and do not fully account for case-mix heterogeneity.
Purpose of the Study:
- Develop a concordance measure robust to censoring and informative about case-mix heterogeneity.
- Quantify the impact of case-mix differences on model discriminative ability.
- Assess the influence of regression coefficient validity on discrimination.
Main Methods:
- Derived a model-based concordance (mbc) for proportional hazards and logistic regression models.
- Developed a calibrated version (c-mbc) incorporating a regression slope for external validation.
- Derived variance formulas for mbc and c-mbc.
- Compared mbc and c-mbc with existing measures via simulation and external validation.
Main Results:
- mbc quantifies case-mix heterogeneity's influence on discriminative ability.
- c-mbc assesses regression coefficient validity and discriminative ability.
- c-mbc demonstrated stability with increasing censoring, unlike Harrell's c-index and Uno's measure.
- Variance estimates for mbc and c-mbc aligned with simulated empirical variances.
Conclusions:
- mbc provides a straightforward quantification of discriminative ability changes due to case-mix.
- c-mbc is a censoring-robust alternative to the c-index, reflecting regression coefficient validity.
- These novel measures enhance the interpretation and reliability of risk prediction models in external validation settings.
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