Marker selection via maximizing the partial area under the ROC curve of linear risk scores

DOI10.1093/biostatistics/kxq052zbMath1437.62653OpenAlexW2150734023WikidataQ33668363 ScholiaQ33668363MaRDI QIDQ3303684

Yuan-chin Ivan Chang, Zhan-Feng Wang

Publication date: 4 August 2020

Published in: Biostatistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biostatistics/kxq052

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (12)

Making classifier performance comparisons when ROC curves intersect ⋮ Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range ⋮ Assessing the diagnostic power of variables measured with a detection limit ⋮ Confidence interval estimation of the Youden index and corresponding cut-point for a combination of biomarkers under normality ⋮ Two simple algorithms on linear combination of multiple biomarkers to maximize partial area under the ROC curve ⋮ A modified area under the ROC curve and its application to marker selection and classification ⋮ Construction of joint confidence spaces for the optimal true class fraction triplet in the ROC space using alternative biomarker cutoffs ⋮ Combining biomarkers by maximizing the true positive rate for a fixed false positive rate ⋮ Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve ⋮ A new way for ranking functional data with applications in diagnostic test ⋮ Binary Markov random fields and interpretable mass spectra discrimination ⋮ The linear combinations of biomarkers which maximize the partial area under the ROC curves

Cites Work

• Unnamed Item
• Linear Combinations of Multiple Diagnostic Markers
• Advances in Neural Networks – ISNN 2005

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