PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
January 1, 2016The R Journal394 citationsOpen Access

easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment

DGDinçer GöksülükSKSelçuk KorkmazGZGökmen Zararsız

Key Points

Key points are not available for this paper at this time.

Abstract

ROC curve analysis is a fundamental tool for evaluating the performance of a marker in a number of research areas, e.g., biomedicine, bioinformatics, engineering etc., and is frequently used for discriminating cases from controls. There are a number of analysis tools which are used to guide researchers through their analysis. Some of these tools are commercial and provide basic methods for ROC curve analysis while others offer advanced analysis techniques and a command-based user interface, such as the R environment. The R environmentg includes comprehensive tools for ROC curve analysis; however, using a command-based interface might be challenging and time consuming when a quick evaluation is desired; especially for non-R users, physicians etc. Hence, a quick, comprehensive, free and easy-to-use analysis tool is required. For this purpose, we developed a user-friendly webtool based on the R language. This tool provides ROC statistics, graphical tools, optimal cutpoint calculation, comparison of several markers, and sample size estimation to support researchers in their decisions without writing R codes. easyROC can be used via any device with an internet connection independently of the operating system. The web interface of easyROC is constructed with the R package shiny.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Göksülük et al. (2016) studied this question.

synapsesocial.com/papers/6a00f6c5da5c1eb07f2dc564https://doi.org/10.32614/rj-2016-042
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Better Decisions through Science2000 · 576 citations
  2. 2Understanding receiver operating characteristic (ROC) curves2006 · 860 citations
  3. 3Signal detection theory and ROC analysis1975 · 1,660 citations
  4. 4ROCR: visualizing classifier performance in R2005 · 3,367 citations
  5. 5Statistical Methods in Diagnostic Medicine2002 · 1,180 citations