Combining multiple biomarkers linearly to maximize the partial area under the ROC curve

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...

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Vydáno v:Statistics in medicine Ročník 37; číslo 4; s. 627 - 642
Hlavní autoři: Yan, Qingxiang, Bantis, Leonidas E., Stanford, Janet L., Feng, Ziding
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Wiley Subscription Services, Inc 20.02.2018
Témata:
Algorithms
Area Under Curve
Biomarkers
Biomarkers - analysis
Biostatistics
Computer Simulation
Datasets
Disease Progression
DNA Methylation - genetics
Humans
Linear Models
Logistic Models
logistic regression
Male
Medical Overuse - prevention & control
Medical statistics
Normal Distribution
optimal linear combination
parametric and nonparametric
pAUC
Prostate cancer
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - genetics
Prostatic Neoplasms - therapy
ROC analysis
ROC Curve
Statistics, Nonparametric
logistic regression
optimal linear combination
pAUC
ROC analysis
parametric and nonparametric
ISSN:0277-6715, 1097-0258, 1097-0258
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.7535