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
Biomarkers of HPV in head and neck squamous cell carcinoma
Caihua Liang,Carmen J. Marsit,Michael D. McClean,Heather H. Nelson,Brock C. Christensen,Robert I. Haddad,John R. Clark,Richard O. Wein,Gregory A. Grillone,E. Andres Houseman,Gordana Halec,Tim Waterboer,Michael Pawlita,Jeffrey F. Krane,Karl T. Kelsey +14 more
TL;DR: A stronger association of HPV presence with prognosis (assessed by all-cause survival) is observed when "HPV-associated" HNSCC is defined using tumor status (HPV DNA status or P16) and HPV E6/E7 serology in combination rather using tumor HPV status alone.
Journal ArticleDOI
Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension
TL;DR: The success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.
Journal ArticleDOI
Using machine learning to predict ICU transfer in hospitalized COVID-19 patients
Fu-Yuan Cheng,Himanshu Joshi,Pranai Tandon,Robert Freeman,Robert Freeman,David Reich,David Reich,Madhu Mazumdar,Roopa Kohli-Seth,Matthew A. Levin,Prem Timsina,Arash Kia +11 more
TL;DR: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h, which could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.
Journal ArticleDOI
Resistance to type 1 interferons is a major determinant of HIV-1 transmission fitness.
Shilpa S. Iyer,Frederic Bibollet-Ruche,Scott Sherrill-Mix,Gerald H. Learn,Lindsey J. Plenderleith,Andrew G. Smith,Hannah J. Barbian,Ronnie M. Russell,Marcos V. P. Gondim,Catherine Y. Bahari,Christiana M. Shaw,Yingying Li,Timothy Decker,Barton F. Haynes,George M. Shaw,Paul M. Sharp,Persephone Borrow,Beatrice H. Hahn +17 more
TL;DR: Characterizing 300 limiting dilution-derived virus isolates from the plasma, and in some instances genital secretions, of eight HIV-1 donor and recipient pairs indicates that transmitted viruses are phenotypically distinct, and that increased IFN resistance represents their most distinguishing property.
Journal ArticleDOI
The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis.
J. Van Schependom,J. Van Schependom,Marie B. D'hooghe,Krista Cleynhens,Mieke D'hooge,Marie-Claire Haelewyck,de Jacques Keyser,Guy Nagels,Guy Nagels +8 more
TL;DR: This paper aims to assess the performance of the SDMT in predicting the outcome of an extensive battery of neuropsychological test batteries for cognitive impairment in MS.
References
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BookDOI
Modern Applied Statistics with S
W. N. Venables,Brian D. Ripley +1 more
TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
Journal ArticleDOI
An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
Journal ArticleDOI
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
TL;DR: A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
Journal ArticleDOI
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
TL;DR: This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data.
Modern Applied Statistics With S
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.