Interactive web-based software for evaluating diagnostic tests and roc curve analyses in health sciences (Annals of Medical Research, 2021)
Aim: This study aims to develop web-based and user-friendly DTROC software in which clinicians and researchers can perform two-and three-dimensional receiver operating characteristic (ROC) curve analyses and calculate diagnostic test performance metrics.Materials and Methods: An illustrative example was presented to implement two- and three-dimensional ROC analyses in medicine. As a case study, three-dimensional ROC analysis in DTROC was applied to the ktemp variable (measurements on the neuropsychometric test for “temporal factor”) in the open-access data set named "AL” (Alzheimer's disease neuropsychometric marker dataset). DTROC web-based software was developed using “plotly”, “pROC”, “dplyr”, “shiny”, “shinydashboard”, “rhandsontable”, “shinyBS”, “DT”, “epiR”, “DiagTest3Grp”, and "the “shiny" libraries.Results: The developed web-based DTROC software allows clinicians and researchers to analyze two and three-dimensional ROC analyzes, ROC curve comparisons, optimum cut-off calculations, required sample size estimation for diagnosis, and calculate metrics for two/more than two-class diagnostic tests without any programming language knowledge or theoretical background about the analyzes mentioned above. When the results for the ktemp variable in the AL data set used in practice were examined, thevolume under the surface was found to be 0.77. According to the application results in DTROC Three-dimensional ROC analysis, it can be said that the ktemp variable has a distinguishing feature in detecting AL.Conclusion: All the comprehensive features of DTROC can be accessed free of charge through a graphical user interface that makes the analysis process very easy for users and automates the analysis process. With all these features, DTROC provides much more comprehensive features and various applications than commercial and free software to achieve two/three-dimensional ROC analyses and calculate diagnostic test metrics for two / more classes.
Keywords: Diagnostic tests; multi-class diagnostic tests; ROC analysis<; ROC curve<; three-dimensional ROC analysis