Software_index

ROCKIT

ROCKIT uses maximum likelihood estimation to fit a binormal ROC curve to:

Continuously-distributed data (e.g., probability assessment on a 0-100 scale or numerical output from a laboratory measurement like a blood count)

AND/OR

Ordinal category data (e.g., discrete confidence ratings, where the categories could be: "definitely or almost definitely negative", " probably negative", "possibly positive", "probably positive", and "definitely or almost definitely positive" .

ROCKIT also calculates the statistical significance of differences between ROC index estimates and parameters. On the basis of a "bivariate binormal" model, it allows for comparison of 2 paired, partially paired, or unpaired datasets (which would represent, for example, different imaging modalities or diagnostic tests) with regard to:

Differences in the binormal ROC curve parameters a and b (related to the difference in the mean and of the standard deviations of the two latest normal distributions used in the fit)

Difference in the areas (Az) under the two estimated binormal ROC curves

Difference between the two true-positive fractions (TPF)s on the two curves at a selected false-positive fraction (FTP)

Platforms: Mac and Windows

References:

ROCKIT, its usage, and the algorithms it employes

Studies that have used ROCKIT

ROCKIT REPLACES ROCFIT, LABROC, CORROC2, CLABROC and INDROC. If the functionality of any of these programs is required, ROCKIT should be used instead. None of the older programs will be updated or modified in the future.

ROCKIT employs our LABROC5 algorithm, a quasi-maximum likelihood approach, to analyze continuously distributed data. The statistical tests performed by ROCKIT allow the conclusions of a study to be generalized to a population of cases, but not to a population of readers. If generalization to populations of both of readers and cases are required, the programs DBM MRMC 2.1 or LABMRMC should be used instead.

New ROCKIT1.1B2, beta version, for Windows available (more stable, more cases can be used)

MRMC 2.1 B 2

Brief Description

MRMC uses the Dorfman-Berbaum-Metz algorithm to compare multiple treatments (e.g., imagining modalities) by using data from multiple readers and multiple cases. This program employs jackknifing and ANOVA techniques. This software is will replace LABMRMC completely in the near future. Improvements to the algorithm are:

	Changes in the ANOVA analysis, following the papers of Steve Hillis et al.
	Introduction of multiple ROC curve models, among which PROPROC and CBM (contaminated binormal model)
	Possibility to use different ROC indices, such as partial areas, sensitivity at specificity, and specificity at sensitivity

Platforms: Currently only Windows (others will follow)

References:

MRMC, its usage, and the algorithms that it employs

This program allows the conclusions drawn from a study to be generalized to both a population of readers and a population of cases

The program requires that the Microsoft .NET Framework Version 1.1 be installed on the system. You can visit http://msdn.microsoft.com/netframework/downloads/howtoget.aspx

to check whether you have the .NET framework installed and to view instructions for downloading it via Windows Update.

When downloading the file, the user should select the “Save” option rather than the “Open” option when downloading because it is necessary to save the zip file to the user’s hard drive for proper extraction. The zip file contains three files: setup.exe, setup.ini, and DBM_MRMC_Beta_Setup.msi. Double-clicking on setup.exe will begin the installation process. The installer will install the program to the Program Files folder on the user’s machine and create a Start menu entry and desktop shortcut for the program. An uninstaller for the program is also created in the Add/Remove program applet in the Control Panel.

Read the user's guide for additional instructions. The user's guide is available at the download page and it is also included in the DBM MRMC installer package.

LABMRMC

Brief Description

LABMRMC uses the Dorfman-Berbaum-Metz algorithm to compare multiple treatments (e.g., imagining modalities) by using data from multiple readers and multiple cases. This program employs jackknifing and ANOVA techniques.

Platforms: Mac and Windows

References:

LABMRMC, its usage, and the algorithms that it employs

Studies that have used LABMRMC

This program allows the conclusions drawn from a study to be generalized to both a population of readers and a population of cases

LABROC4

Brief Description

LABROC4 uses maximum likelihood estimation to fit a single conventional binormal ROC curve to continuously-distributed data (e.g., probability assessment on a 0-100 scale or numerical output from a laboratory measurement such as blood count).

This program provides standard error estimates that can be used to evaluate the statistical significance of the differences between unpaired ROC estimates

Platforms: Linux and Mac, available only through direct request

References:

LABROC4, its usage, and the algorithms it employs

Studies that have used LABROC4

This program uses a true maximum-likelihood approach rather than the quasi-maximum likelihood approach (LABROC5) that is used in ROCKIT. It will be phased out and incorporated into ROCKIT at some point in the near future.

PROPROC

Brief Description

PROPROC fits a single ROC curve by maximum-likelihood estimation (given ordinal category data) or by quasi maximum-likelihood estimation (given continuously-distributed data). However, unlike ROCKIT or LABROC4, PROPROC employs a "proper" binormal model that always provides convex fitted curves. Thus, this program allows the fitting of degenerate data sets (datasets which can be fit with vertical and horizontal straight line segments, which are impossible to fit using the conventional binormal model) and data sets that would yield non-monotonic (i.e., non-convex) fits with the conventional binormal model.

This program provides standard error estimates that can be used to evaluate the statistical significance of the differences between unpaired ROC estimates

Platforms: Linux, Mac and Windows, available only through direct request

References

PROPROC, its usage, and the algorithms it employs

Studies that have used PROPROC

This program is unique among our ROC software in the sense that it is the only program that employs the "proper" binormal model

Software programs available from the Kurt Rossmann Laboratories

Download software

Differences in the binormal ROC curve parameters a and b (related to the difference in the mean and of the standard deviations of the two latest normal distributions used in the fit)

Difference in the areas (Az) under the two estimated binormal ROC curves

Difference between the two true-positive fractions (TPF)s on the two curves at a selected false-positive fraction (FTP)

Platforms: Mac and Windows

LABMRMC

LABROC4 uses maximum likelihood estimation to fit a single conventional binormal ROC curve to continuously-distributed data (e.g., probability assessment on a 0-100 scale or numerical output from a laboratory measurement such as blood count).

This program provides standard error estimates that can be used to evaluate the statistical significance of the differences between unpaired ROC estimates

Platforms: Linux and Mac, available only through direct request

Platforms: Linux, Mac and Windows, available only through direct request

Platforms: Mac and Windows

It requires Microsoft Excel 5.0

Brief Description