Combining multiple biomarkers linearly to maximize the partial area under the ROC curve.
Stat Med
; 37(4): 627-642, 2018 02 20.
Article
in En
| MEDLINE
| ID: mdl-29082535
ABSTRACT
It is now common in clinical practice to make clinical decisions based on combinations of multiple biomarkers. In this paper, we propose new approaches for combining multiple biomarkers linearly to maximize the partial area under the receiver operating characteristic curve (pAUC). The parametric and nonparametric methods that have been developed for this purpose have limitations. When the biomarker values for populations with and without a given disease follow a multivariate normal distribution, it is easy to implement our proposed parametric approach, which adopts an alternative analytic expression of the pAUC. When normality assumptions are violated, a kernel-based approach is presented, which handles multiple biomarkers simultaneously. We evaluated the proposed as well as existing methods through simulations and discovered that when the covariance matrices for the disease and nondisease samples are disproportional, traditional methods (such as the logistic regression) are more likely to fail to maximize the pAUC while the proposed methods are more robust. The proposed approaches are illustrated through application to a prostate cancer data set, and a rank-based leave-one-out cross-validation procedure is proposed to obtain a realistic estimate of the pAUC when there is no independent validation set available.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Biomarkers
/
Area Under Curve
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
/
Male
Language:
En
Journal:
Stat Med
Year:
2018
Document type:
Article
Affiliation country: