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HELLENIC COMPLEX SYSTEMS LABORATORY

 

HCSL Publications

Diagnostic Accuracy

1. Hatjimihail AT. Resource review: “Whiting P. Quality of diagnostic accuracy studies: The development, use, and evaluation of QUADAS. Bristol: P E Whiting, 2006.” Evidence-Based Medicine 2006:11;189. 

Full text in Evidence Based Medicine

2. Hatjimihail AT. Receiver Operating Characteristic Plots and Uncertainty of Measurement. The Wolfram Demonstrations Project, 2008.

Abstract 

This Demonstration compares two receiver operating characteristic   (ROC) plots of two diagnostic tests (first test: blue plot, second test: orange plot) measuring the same measurand, for normally distributed healthy and diseased populations, for various values of the mean and standard deviation of the populations, and of the uncertainty of measurement   of the tests. A normal distribution of the uncertainty is assumed. The ratio of the areas under the ROC curves of the two diagnostic tests is calculated. The six parameters that you can vary using the sliders are measured in arbitrary units.

Uncertainty and ROC Plots

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code at Wolfram Research Demonstrations Project

3. Hatjimihail AT. Uncertainty of Measurement and Areas Over and Under the ROC Curves. The Wolfram Demonstrations Project, 2009.

Abstract 

This Demonstration compares the ratios of the areas under the curve (AUC) and the ratios of the areas over the curve (AOC) of the receiver operating characteristic (ROC) plots of two diagnostic tests (ratio of the AUC of the first test to the AUC of the second test: blue plot, ratio of the AOC of the first test to the AOC of the second test: orange plot). The two tests measure the same measurand, for normally distributed healthy and diseased populations, for various values of the mean and standard deviation of the populations, and of the uncertainty of measurement of the tests. A normal distribution of the uncertainty is assumed. The uncertainty of the first test is defined. It is assumed that the uncertainty of the second test is greater than the uncertainty of the first test and varies up to a user defined upper bound. The six parameters that you can vary using the sliders are measured in arbitrary units.

Uncertainty, AUC and AOC

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code at Wolfram Research Demonstrations Project

4. Hatjimihail AT. Uncertainty of Measurement and Diagnostic Accuracy Measures. The Wolfram Demonstrations Project, 2009.

Abstract 

This Demonstration compares various diagnostic accuracy measures of two diagnostic tests. The two tests measure the same measurand, for normally distributed healthy and diseased populations, for various values of the prevalence of the disease, of the mean and standard deviation of the populations, and of the uncertainty of measurement of the tests. A normal distribution of the uncertainty is assumed. The mean and the standard deviation of each population and the uncertainty of each test are measured in arbitrary units. The measures compared are the positive prognostic value (PPV), the negative prognostic value (NPV), the (diagnostic) odds ratio (OR), the likelihood ratio for a positive result (LR+), and the likelihood ratio for a negative result (LR-). The measures are calculated versus the sensitivity or the specificity of each test. That can be selected by pressing the respective button. The types of plot are: both measures (first test: blue plot, second test: orange plot), partial derivatives of both measures with respect to uncertainty (first test: blue plot, second test: orange plot), difference, and ratio of the two measures. The types of plot can be selected by pressing the respective buttons, while the seven parameters can vary using the sliders.

Uncertainty and Diagnostic Accuracy

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code at Wolfram Research Demonstrations Project

5. Chatzimichail T. Analysis of Diagnostic Accuracy Measures. The Wolfram Demonstrations Project, 2015.

Abstract 

This Demonstration shows various diagnostic accuracy measures of a diagnostic test for normally distributed healthy and diseased populations, for various values of the prevalence of the disease, and of the mean and standard deviation of the populations. The mean and the standard deviation of each population are measured in arbitrary units. The measures shown are the positive prognostic value (PPV), the negative prognostic value (NPV), the (diagnostic) odds ratio (OR), the likelihood ratio for a positive result (LR+), and the likelihood ratio for a negative result (LR-). The measures are calculated versus the sensitivity or the specificity of each test. That can be selected by clicking the respective button.

Diagnostic Accuracy Measures

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code at Wolfram Research Demonstrations Project

6. Chatzimichail T. Correlation of Positive and Negative Prognostic Values. The Wolfram Demonstrations Project, 2015.

Abstract 

This Demonstration examines the correlation of the negative prognostic value (NPV) and the positive prognostic value (PPV) of a diagnostic test for normally distributed healthy and diseased populations. Differing levels of prevalence of the disease are considered. The mean and standard deviation of the populations, measured in arbitrary units, are used.

PPV vs NPV

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code at Wolfram Research Demonstrations Project