pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin,Natacha Turck,Alexandre Hainard,Natalia Tiberti,Frédérique Lisacek,Jean-Charles Sanchez,Markus Müller +6 more
TLDR
pROC as mentioned in this paper is a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.Abstract:
Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/
under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.read more
Citations
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Journal ArticleDOI
ROC and AUC with a Binary Predictor: a Potentially Misleading Metric
TL;DR: Overall, it is shown using a linear interpolation from the ROC curve with binary predictors corresponds to the estimated AUC, which is most commonly done in software, which the authors believe can lead to misleading results.
Journal ArticleDOI
The ground glass opacity component can be eliminated from the T-factor assessment of lung adenocarcinoma †
Tomohiro Murakawa,Chihiro Konoeda,Takuya Ito,Yuta Inoue,Atsushi Sano,Kazuhiro Nagayama,Jun Nakajima +6 more
TL;DR: The GGO component showed little influence on recurrence and was solely dependent on the solid component size, while the maximum tumour diameter measured in the mediastinal window was a better prognostic factor than the maximum tumor diameter in the lung window.
Journal ArticleDOI
Predictive score for mortality in patients with COPD exacerbations attending hospital emergency departments
José M. Quintana,Cristóbal Esteban,Anette Unzurrunzaga,Susana Garcia-Gutierrez,Nerea González,Irantzu Barrio,Inmaculada Arostegui,Iratxe Lafuente,Marisa Baré,Nerea Fernández-de-Larrea,Silvia Vidal +10 more
TL;DR: Five clinical predictors easily available in the ED, and also in the primary care setting, can be used to create a simple and easily obtained score that allows clinicians to stratify patients with eCOPD upon ED arrival and guide the medical decision-making process.
Journal ArticleDOI
Gentle Introduction to the Statistical Foundations of False Discovery Rate in Quantitative Proteomics
TL;DR: This article aims to provide an easy-to-understand explanation of these four notions (and a few other related ones) from a perspective that largely relies on intuition, addressing mainly protein quantification but also, to some extent, peptide identification.
Journal ArticleDOI
Genetic association and risk scores in a chronic obstructive pulmonary disease meta-analysis of 16,707 subjects
Robert Busch,Brian D. Hobbs,Jin Zhou,Peter J. Castaldi,Michael J. McGeachie,Megan Hardin,Iwona Hawryłkiewicz,Pawel Sliwinski,Jae-Joon Yim,Woo Jin Kim,Deog Kyeom Kim,Alvar Agusti,Barry J. Make,James D. Crapo,Peter M.A. Calverley,Claudio F. Donner,David A. Lomas,Emiel F.M. Wouters,Jørgen Vestbo,Ruth Tal-Singer,Per Bakke,Amund Gulsvik,Augusto A. Litonjua,David Sparrow,Peter D. Paré,Robert D. Levy,Stephen I. Rennard,Terri H. Beaty,John E. Hokanson,Edwin K. Silverman,Michael H. Cho +30 more
TL;DR: A large genetic association analysis identified associations with severe COPD near PPIC and SERPINA1 and a risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function.
References
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