Evaluation of Area under the Constant Shape Bi-Weibull ROC Curve

Abstract

The Receiver Operating Characteristic (ROC) curve generated based on assuming a constant shape Bi-Weibull distribution is studied. In the context of ROC curve analysis, it is assumed that biomarker values from controls and cases follow some specific distribution and the accuracy is evaluated by using the ROC model developed from that specified distribution. This article assumes that the biomarker values from the two groups follow Weibull distributions with equal shape parameter and different scale parameters. The ROC model, area under the ROC curve (AUC), asymptotic and bootstrap confidence intervals for the AUC are derived. Theoretical results are validated by simulation studies.

Bi-power Lindley ROC curve analysis: Application to Malignant Tumors Diagnosis

“…Additionally, they developed a user-friendly software package specifically tailored for diagnostic test accuracy metaanalysis. Pundir and Amala (2014) discussed the ROC analysis under the bi-Weibull distribution. Bertail et al (2008) discussed confidence bands for the ROC curve and considered a re-sampling procedure based on a smoothed version of the empirical distribution.…”

Evaluation of Diagnostic Accuracy and its Standard Error using Constant Shape Weibull Mixture ROC Curve

“…They compared the bias, mean absolute error, total mean error and time completion of the algorithm using different method of estimation by simulation studies. Pundir and Amala [2014] proposed and discussed the characteristics of the constant shape weibull ROC Curve.…”