Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 698320, 7 pages
Research Article

Comparison of Semiparametric, Parametric, and Nonparametric ROC Analysis for Continuous Diagnostic Tests Using a Simulation Study and Acute Coronary Syndrome Data

1Department of Biostatistics and Medical Informatics, School of Medicine, Eskisehir Osmangazi University, 26480 Eskisehir, Turkey
2Department of Cardiology, Private Bursa Anadolu Hospital, Izmir Yolu 105, 16120 Nilufer/Bursa, Turkey
3Department of Cardiology, School of Medicine, Eskisehir Osmangazi University, 26480 Eskisehir, Turkey

Received 14 March 2012; Revised 20 April 2012; Accepted 24 April 2012

Academic Editor: Guang Wu

Copyright © 2012 Ertugrul Colak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We aimed to compare the performance of three different individual ROC methods (one from each of the broad categories of parametric, nonparametric and semiparametric analysis) for assessing continuous diagnostic tests: the binormal method as a parametric method, an empirical approach as a nonparametric method, and a semiparametric method using generalized linear models (GLM). We performed a simulation study with various sample sizes under normal, skewed, and monotone distributions. In the simulations, we used estimates of the ROC curve parameters 𝑎 and 𝑏 , estimates of the area under the curve (AUC), the standard errors and root mean square errors (RMSEs) of these estimates, and the 95% AUC confidence intervals for comparison. The three methodologies were also applied to an acute coronary syndrome dataset in which serum myoglobin levels were used as a biomarker for detecting acute coronary syndrome. The simulation and application studies suggest that the semiparametric ROC analysis using GLM is a reliable method when the distributions of the diagnostic test results are skewed and that it provides a smooth ROC curve for obtaining a unique cutoff value. A sample size of 50 is sufficient for applying the semiparametric ROC method.