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plotROC: A Tool for Plotting ROC Curves.

Michael C Sachs1

  • 1Michael C. Sachs, Unit of Biostatistics, Nobels väg 13, Karolinska Institutet, 17177 Stockholm, Sweden sachsmc@gmail.com, Telephone: +46 07 65 78 09 83.

Journal of Statistical Software
|January 29, 2019
PubMed
Summary
This summary is machine-generated.

Receiver operating characteristic (ROC) curve plots are common in oncology research but often poorly designed. A new R package and web application offer improved tools for creating informative ROC curve visualizations.

Keywords:
ROC curvesgraphicsinteractiveplots

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Area of Science:

  • Medical research
  • Biostatistics
  • Oncology

Background:

  • Receiver operating characteristic (ROC) curve plots are widely used in medical research, particularly in oncology, to evaluate various biomarkers.
  • These plots are intended to display the operating characteristics of continuous diagnostic tests across all possible values.
  • However, current trends indicate that ROC curve plots are frequently ineffective due to poor design, obscuring critical information.

Purpose of the Study:

  • To assess current trends in the usage and design elements of ROC curve plots in major oncology journals.
  • To identify shortcomings in existing tools for generating ROC curve plots.
  • To introduce a new R package and accompanying web application designed to create more informative and user-friendly ROC curve visualizations.

Main Methods:

  • A review of ROC curve plots from a sample of major oncology journals was conducted.
  • An R package was developed with functions for creating informative ROC curve plots.
  • A web application was created to provide broader accessibility to these visualization tools.

Main Results:

  • The review revealed that ROC curve plots in oncology literature are often statistically ineffective and poorly designed.
  • The developed R package offers sensible defaults and a simple interface for creating print or interactive web-based ROC plots.
  • The web application extends the reach of these improved visualization tools to a wider scientific audience.

Conclusions:

  • Existing ROC curve plot designs in oncology research often hinder effective data interpretation.
  • The new R package and web application provide enhanced tools for generating clear and informative ROC curve visualizations.
  • These tools aim to improve the quality and impact of ROC curve analysis in scientific publications and presentations.