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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics

Open Access Correction

Correction: A comparison of statistical methods for the detection of hepatocellular carcinoma based on serum biomarkers and clinical variables

Mengjun Wang1, Anand Mehta1, Timothy M Block1, Jorge Marrero2, Adrian M Di Bisceglie3 and Karthik Devarajan4*

  • * Corresponding author: Karthik Devarajan

Author Affiliations

1 Drexel University College of Medicine, 3508 Old Easton Rd, Doylestown, PA 18902, USA

2 Division of Gastroenterology, University of Michigan, 3912 Taubman Center, Ann Arbor, MI 48109, USA

3 Saint Louis University School of Medicine, 1402 S. Grand FDT 12th Floor, St. Louis, MO 63104, USA

4 Department of Biostatistics and Bioinformatics, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 18901, USA

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BMC Medical Genomics 2013, 6(Suppl 3):S11  doi:10.1186/1755-8794-6-S3-S11


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1755-8794/6/S3/S11


Published:20 December 2013

© 2013 Wang et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Correction

Our original article was published in BMC Medical Genomics in the supplement containing selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012 (IEEE BIBM 2012) [1]. After publication, it was noticed that the ROC curves in Figures 1, 2, 3, 4 displayed Sensitivity vs. Specificity rather than Sensitivity vs. 1-Specificity, as labeled. These figures have been reproduced here in the correct format, displaying Sensitivity vs. 1-Specificity, and should replace the corresponding figures in the original article. However, AUC values remain unaffected by this change.

thumbnailFigure 1. ROC curves based on multivariable stepwise penalized logistic regression models (stepPLR) using the stratified male-only subset. The age-adjusted final model for λ = 0.1 showed the best performance in terms of AUC. A clear distinction is seen in the ROC curves for age-adjusted models compared to age-unadjusted models. Age-adjusted models demonstrated superior performance overall across all choices of λ. See Table 1 for detailed results and the text for discussion of these results.

thumbnailFigure 2. ROC curves based on multivariable stepwise penalized logistic regression models (stepPLR) adjusting for gender effect. Models that are also adjusted for age effect outperformed those that did not control for age, across all choices of the parameter λ. The age-adjusted final model for λ = 0.1 showed the best performance in terms of AUC. See Table 1 for detailed results and the text for discussion of these results.

thumbnailFigure 3. ROC curves based on multivariable model-based CART analyses (mob) using the stratified male-only subset. Age-adjusted models demonstrated superior performance in terms of AUC. A clear distinction is seen in the ROC curves for age-adjusted models (solid lines) compared to age-unadjusted models (dotted lines). See Table 1 for detailed results and the text for discussion of these results.

thumbnailFigure 4. ROC curves based on multivariable model-based CART analyses (mob) incorporating gender and/or age. Age-adjusted models demonstrated superior performance in terms of AUC when gender effect is accounted for in each model. A clear distinction is seen in the ROC curves for age-adjusted models (solid lines) compared to age-unadjusted models (dotted lines). Table 1 lists the performance measures for these models. A detailed discussion of the results is provided in the text.

Acknowledgements

This article has been published as part of BMC Medical Genomics Volume 6 Supplement 3, 2013: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/6/S3.

References

  1. Wang M, Mehta A, Block TM, Marrero J, Di Bisceglie AM, Devarajan K: A comparison of statistical methods for the detection of hepatocellular carcinoma based on serum biomarkers and clinical variables.

    BMC Medical Genomics 2013, 6(Suppl 3):S9. BioMed Central Full Text OpenURL