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Comparison of Semiparametric, Parametric, and Nonparametric ROC Analysis for Continuous Diagnostic Tests Using a Simulation Study and Acute Coronary Syndrome Data
Joint Authors
Colak, Ertugrul
Oner, Setenay
Bal, Cengiz
Mutlu, Fezan
Ozdamar, Kazim
Cavusoglu, Yuksel
Gok, Bulent
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-28
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Colak, Ertugrul& Mutlu, Fezan& Bal, Cengiz& Oner, Setenay& Ozdamar, Kazim& Gok, Bulent…[et al.]. 2012. Comparison of Semiparametric, Parametric, and Nonparametric ROC Analysis for Continuous Diagnostic Tests Using a Simulation Study and Acute Coronary Syndrome Data. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-491524
Modern Language Association (MLA)
Colak, Ertugrul…[et al.]. Comparison of Semiparametric, Parametric, and Nonparametric ROC Analysis for Continuous Diagnostic Tests Using a Simulation Study and Acute Coronary Syndrome Data. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-491524
American Medical Association (AMA)
Colak, Ertugrul& Mutlu, Fezan& Bal, Cengiz& Oner, Setenay& Ozdamar, Kazim& Gok, Bulent…[et al.]. Comparison of Semiparametric, Parametric, and Nonparametric ROC Analysis for Continuous Diagnostic Tests Using a Simulation Study and Acute Coronary Syndrome Data. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-491524
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-491524