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A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding a single ideal biomarker of a high level of both sensitivity and specificity is not an easy task; especially when a high specificity is required for a population screening tool. Combining multiple biomarkers is a promising alternative and can provide a better overall performance than the use of a single biomarker. It is known that the area under the receiver operating characteristic (ROC) curve is most popular for evaluation of a diagnostic tool. In this study, we consider the criterion of the partial area under the ROC curve (pAUC) for the purpose of population screening. Under the binormality assumption, we obtain the optimal linear combination of biomarkers in the sense of maximizing the pAUC with a pre-specified specificity level. Furthermore, statistical testing procedures based on the optimal linear combination are developed to assess the discriminatory power of a biomarker set and an individual biomarker, respectively. Stepwise biomarker selections, by embedding the proposed tests, are introduced to identify those biomarkers of statistical significance among a biomarker set. Rather than for an exploratory study, our methods, providing computationally intensive statistical evidence, are more appropriate for a confirmatory analysis, where the data has been adequately filtered. The applicability of the proposed methods are shown via several real data sets with a moderate number of biomarkers.
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Etzioni, R., Kooperberg, C., Pepe, M., Smith, R. and Gann, P. H. (2003). Combining biomarkers to detect disease with application to prostate cancer. Biostatistics 4, 523-538.
Hsu, M.-J. and Hsueh, H.-M. (2012). The linear combinations of biomarkers which maximize the partial area under the ROC curves. Computational Statistics, to appear. DOI: 10.1007/s00180-012-0321-5.
Komori, O. and Eguchi, S. (2010). A boosting method for maximizing the partial area under the ROC curve. BFC Bioinformatics 11, 314-330.
Lasko, T. A., Bhagwat, J. G., Zou, K. H. and Ohno-Machado L. (2005). The use of receiver operating characteristic curves in biomedical informatics. Journal of Biomedical Informatics 38, 404-415.
Lin, H., Zhou, L., Peng, H. and Zhou, X.-H. (2011). Selection and combination of biomarkers using ROC method for disease classification and prediction. The Canadian Journal of Statistics 39, 324-343.
Liu, A., Schisterman, E. F. and Zhu, Y. (2005). On linear combinations of biomarkers to improve diagnostic accuracy. Statistics in Medicine 24, 37-47.
Ma, S. and Huang, J. (2005). Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics 21, 4356-4362.
Ma, S. and Huang, J. (2007). Combining multiple markers for classification Using ROC. Biometrics 63, 751-757.
Madu, C. O. and Lu, Y. (2010). Novel diagnostic biomarkers for prostate cancer. Journal of Cancer 1, 150-177.
Marrocco, C., Duin, R. P. W. and Tortorella, F. (2008). Maximizing the area under the ROC curve by pairwise feature combination. Pattern Recognition 41, 1961-1974.
National Cancer Institute: PDQ®;Prostate Cancer Screening. Bethesda, MD: National Cancer Institute. Date last modified \(\langle 06/08/2012\rangle\). Available at: http://www.cancer.gov/cancertopics/pdq/screening/prostate/HealthProfessional/Page3\#Section_67
Pepe, M. S. and Thompson, M. L. (2000). Combining diagnostic test results to increase accuracy. Biostatistics 1, 123-140.
Pepe, M. S., Longton, G., Anderson, G. L. and Schummer, M. (2003). Selecting differentially expressed genes from microarray experiments. Biometrics 59, 133-142.
Pepe, M. S., Cai, T. and Longton, G. (2006). Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62, 221-229.
Ricamato, M. T. and Tortorella, F. (2011). Partial AUC maximization in a linear combination of dichotomizers. Pattern Recognition 44, 2669-2677.
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C. and Muller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BFC Bioinformatics 12, 77-84.
Su, J. Q. and Liu, J. S. (1993). Linear combinations of multiple diagnostic markers. Journal of the American Statistical Association 88, 1350-1355.
Shao, J. (1999). Mathematical Statistics. Springer-Verlag Inc.
Turck, N., Vutskits, L., Sanchez-Pena, P., Robin, X., Hainard, A., Gex-Fabry, M., Fouda, C., Bassem, H., Mueller, M., Lisacek, F., Puybasset, L. and Sanchez, J.-C. (2010). A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage. Intensive Care Medicine 36, 107-115.
Vaart, A. W. van der (1998). Asymptotic Statistics. Cambridge University Press.
Wang, Z. and Chang, Y.-C. I. (2011). Marker selection via maximizing the partial area under the ROC curve of linear risk scores. Biostatistics 12, 369-385.
Zhou, X. H., Chen, B., Xie, Y. M., Tian, F., Liu, H. and Liang, X. (2012). Variable selection using the optimal ROC curve: An application to a traditional Chinese medicine study on osteoporosis disease. Statistics in Medicine 31, 628-635.
- Biomarker Selection in Medical Diagnosis
Yuan-Chin Ivan Chang
- Springer New York