Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, and Other Metrics)

Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. The initial package analyzes the reader-average area under the receiver operating characteristic (ROC) curve with U-statistics according to Gallas, Bandos, Samuelson, and Wagner 2009 . Additional functions analyze other endpoints with U-statistics (binary performance and score differences) following the work by Gallas, Pennello, and Myers 2007 . Package development and documentation is at <>.


iMRMC 1.1.0

  • Added functions uStat11.jointD and uStat11.conditionalD. These functions calculate the mean and variance of the indicated U-statistic kernel, which is a function of the input scores.

Reference manual

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1.2.3 by Brandon Gallas, 4 months ago

Browse source code at

Authors: Brandon Gallas

Documentation:   PDF Manual  

CC0 license

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System requirements: Java JDK 1.7 or higher

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