title: Comparison of semiparametric, parametric, and nonparametric ROC analysis for continuous diagnostic tests using a simulation study and acute coronary syndrome data.
abstract: 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.
authors: Department of Biostatistics and Medical Informatics, School of Medicine, Eskisehir Osmangazi University, 26480 Eskisehir, Turkey. ecolak@ogu.edu.tr
authors: Ertugrul
authors: E
authors: Colak
authors: Fezan
authors: F
authors: Mutlu
authors: Cengiz
authors: C
authors: Bal
authors: Setenay
authors: S
authors: Oner
authors: Kazim
authors: K
authors: Ozdamar
authors: Bulent
authors: B
authors: Gok
authors: Yuksel
authors: Y
authors: Cavusoglu
mesh_terms: D054058:Acute Coronary Syndrome / Q000175:diagnosis*; D000465:Algorithms; D019540:Area Under Curve; D003198:Computer Simulation; D016001:Confidence Intervals; D003955:Diagnostic Tests, Routine / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D018401:Sample Size