# 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, 2007.

#### 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 nondiseased 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.

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code (Revised on 09/04/2018)

### 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 nondiseased 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.

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code (Revised on 06/04/2018)

### 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 nondiseased 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 predictive value (PPV), the
negative predictive 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.

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code (Revised on 06/04/2018)

### 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 nondiseasdy 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 predictive value (PPV), the
negative predictive 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.

Snapshot of the Demonstration

Demonstration at
Wolfram Research Demonstrations Project

Mathematica source code ((Revised on 30/03/2018)

### 6. Chatzimichail T. Correlation of Positive and
Negative Predictive Values. The Wolfram Demonstrations Project, 2018.

#### Abstract

This Demonstration examines the
correlation of the negative predictive value (NPV) and
the positive predictive value (PPV) of a
diagnostic test for normally distributed nondiseasdy 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.

Snapshot of the Demonstration

Demonstration at Wolfram
Research Demonstrations Project

Mathematica source code

### 7. Chatzimichail T, Hatjimihail AT. Analysis
of Diagnostic Accuracy Measures for Two Combined Diagnostic Tests. The Wolfram
Demonstrations Project, 2018.

#### Abstract

This Demonstration shows plots of various
accuracy measures for two combined diagnostic tests applied at a single point
in time on nondiseased and diseased populations. This is done for differing
prevalence of the disease, taking into account the means and standard
deviations of the populations and the respective correlation coefficients. The
means and standard deviations are expressed in arbitrary units.

Snapshot of the Demonstration

Demonstration at Wolfram Research Demonstrations Project

Mathematica source code (Revised on 10/04/2018)