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

The original article was published in BMC Medical Genomics 2013 6:S9

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.

Figure 1
figure1

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.

Figure 2
figure2

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.

Figure 3
figure3

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.

Figure 4
figure4

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.

References

  1. 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): S9-10.1186/1755-8794-6-S3-S9.

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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.

Author information

Correspondence to Karthik Devarajan.

Additional information

The online version of the original article can be found at 10.1186/1755-8794-6-S3-S9

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