Measures for evaluation of prognostic improvement under multivariate normality for nested and nonnested models.

ABSTRACT: When comparing performances of two risk prediction models, several metrics exist to quantify prognostic improvement, including the change in the area under the Receiver Operating Characteristic curve, the Integrated Discrimination Improvement, the Net Reclassification Index at event rate, the change in Standardized Net Benefit, the change in Brier score, and the change in scaled Brier score. We explore the behavior and interrelationships between these metrics under multivariate normality in nested and nonnested model comparisons. We demonstrate that, within the framework of linear discriminant analysis, all six statistics are functions of squared Mahalanobis distance, a robust metric that properly measures discrimination by quantifying the separation between the risk scores of events and n

SUBMITTER: Enserro DM 

PROVIDER: S-EPMC6827341 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature