Abstract
In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.
Original language | English (US) |
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Pages (from-to) | 3725-3732 |
Number of pages | 8 |
Journal | Journal of Statistical Planning and Inference |
Volume | 139 |
Issue number | 10 |
DOIs | |
State | Published - Oct 1 2009 |
Externally published | Yes |
Keywords
- Generalized pivot
- Generalized test variable
- Optimal linear combinations
- Receiver operating characteristic (ROC) curve
ASJC Scopus subject areas
- Applied Mathematics
- Statistics and Probability
- Statistics, Probability and Uncertainty