plotROC: A Tool for Plotting ROC Curves
By Michael C. Sachs. Journal of Statistical Software, 2017
342 citations recorded by the source corpus.
Plots of the receiver operating characteristic (ROC) curve are ubiquitous in medical research. Designed to simultaneously display the operating characteristics at every possible value of a continuous diagnostic test, ROC curves are used in oncology to evaluate screening, diagnostic, prognostic and predictive biomarkers. I reviewed a sample of ROC curve plots from the major oncology journals in order to assess current trends in usage and design elements. My review suggests that ROC curve plots are often ineffective as statistical charts and that poor design obscures the relevant information the chart is intended to display. I describe my new R package that was created to address the shortcomings of existing tools. The package has functions to create informative ROC curve plots, with sensible defaults and a simple interface, for use in print or as an interactive web-based plot. A web application was developed to reach a broader audience of scientists who do not use R.
Subfields: Artificial Intelligence, Physiology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/be888c47c71f2e05520a17a84bebe46e52699cd5
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Citations in the corpus
- Receiver operating characteristic curve: overview and practical use for clinicians (2022, methodology): “The web tool for the ROC curve analysis based on R, which includes easyROC and plotROC [33,34], is a web-based application that uses the R packages plyr, pROC, and OptimalCutpoints to perform ROC curve analyses, extending the functions of multiple ROC packages in R so that researchers can perform RO”