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Parametric receiver operating characteristic curve analysis using mathematica
1Department of Medicine (M/C 787), The Section of General Internal Medicine, University of Illinois, 840 South Wood Street, Chicago 60612, USA.
Computer Methods and Programs in Biomedicine
|June 29, 2002
Summary
rocMath is a Mathematica program for parametric receiver operating characteristic (ROC) curve analysis. It offers enhanced features like user-specified confidence limits and flexible distribution options for improved diagnostic test evaluation.
Area of Science:
- Biostatistics
- Medical Informatics
- Computer Science
Background:
- Receiver operating characteristic (ROC) curve analysis is crucial for evaluating diagnostic tests.
- Existing public domain software for ROC analysis has limitations.
- Parametric methods offer advantages in ROC analysis.
Purpose of the Study:
- To introduce 'rocMath', a Mathematica program for parametric ROC curve analysis.
- To highlight the unique features and advantages of 'rocMath' compared to other ROC software.
- To provide a detailed description of the program's theory and implementation.
Main Methods:
- Development of 'rocMath' using Mathematica's symbolic and numeric computation capabilities.
- Implementation of parametric ROC curve analysis.
- Inclusion of optional arguments for user-specified confidence limits and plots.
- Support for both logistic and standard normal distributions.
Main Results:
- 'rocMath' provides user-specified pointwise confidence limits on ROC curve area and true-positive rates.
- The program generates ROC plots with data points, fitted curves, and confidence bands.
- ROC analysis can be performed using logistic or standard normal distributions.
- Mathematica's capabilities ensure coding efficiency.
Conclusions:
- 'rocMath' offers advanced features for parametric ROC curve analysis.
- The program's flexibility and efficiency make it a valuable tool for researchers.
- The code is adaptable for various ROC analysis applications, with limitations acknowledged.
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