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Programs that fit ROC curves and test differences between them

ROCKIT

 Brief Description

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”, 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 binomial” 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 binomial 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 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(FPF).

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 program LABMRMC should be used instead.

DOWNLOAD ROCKIT1.1B2 (for Windows)

DOWNLOAD ROCKIT1.1B2 (for Mac)

LABMRMC

 Brief Description

MRMC uses the Dorfman-Berbaum-Metz algorithm to compare multiple treatments (e.g., imaging modalities) by using data from multiple readers and multiple cases.

DOWNLOAD LABMRMC (for Windows)

DOWNLOAD LABMRMC1.0B3 (for Mac)

DBMMRMC

 Brief Description

DBM MRMC is an extension of LABMRMC from Charles Metz and MRMC from Kevin Berbaum and Donald Dorfman.

Sorry, but the web site for DBM-MRMC has been disconnected.

DOWNLOAD DBM_MRMC_2_2_Installer-Build_3
Auxiliary ROC software

PlotROC.xls

 Brief Description

This is a Microsoft Excel 5.0 macro sheet which takes the a and b parameter values of the conventional binormal model and plots an ROC curve suitable for presentation and publication
Platform: Mac and Windows. It requires Microsoft Excel 5.0

DOWNLOAD PlotROC.xls

ROCPWR

 Brief Description

This program predicts the power of the statistical test that is performed by ROCKIT with fully-paired data. It requires, as Input, values of the conventional binomial parameters a and b for each of the two ROC curves (which can be obtained by guesswork or by running ROCKIT or LABROC4 with plot-study data). ROCPWR outputs a table of estimated statistical power for a variety of case sample sizes.
Platforms: Windows

DOWNLOAD rocpwrpc (for Windows)