doAUCmrmc: MRMC analysis for arbitrary design dataset

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

MRMC analysis for arbitrary design dataset

Description

Execute a Multi-Reader, Multi-Case (MRMC) analysis of ROC data from imaging studies where clinicians (readers) evaluate patient images (cases). An overview of this software, including references presenting details on the methods, can be found HERE or as an entry in the FDA/CDRH Regulatory Science Tool Catalog HERE.

Usage

doAUCmrmc(data, flagROC = FALSE)

Arguments

data	an iMRMC formatted data frame, see dfMRMC_example
flagROC	boolean indicating if ROC results should be computed.

Value

The MRMC analysis results as a list, below is a quick summary:

• summaryMRMC, list a list of summary study design information.

  • nM, num number of modalities

  • nR, num number of readers

  • nC.neg, num number of signal-present caeses

  • nC.pos, num number of signal-absent cases

  • modalites, char names of modalities

  • readers, char names of modalities

  • cases.neg, char names of modalities

  • cases.pos, char names of modalities

• perReader.full, data.frame this data frame contains the performance results for each reader and modality comparison. The analysis returns the final AUC results and the moments, coefficients of those moments. Key variables of this data frame are AUC.1 (where '.1' indicates the row's reader and modality '.1' pair), AUC.2 ('.2' indicates the '.2' reader and modality pair), and covAUC.

• Ustat.full, data.frame this data frame contains the reader-average AUC performance results. The analysis results are based on U-statistics. Key variables of this data frame are AUC.1, AUC.2, AUC1minusAUC2 and the corresponding variances, confidence intervals, degrees of freedom and p-values.

• ROC, list each object of this list is an object containing an ROC curve. There is an ROC curve for every combination of reader and modality. For every modality, there are also four average ROC curves. These are discussed in Chen2014_Br-J-Radiol_v87p20140016. The diagonal average averages the reader-specific ROC curves along y = -x + b for b in (0,1). The horizontal average averages the reader specific ROC curves along y = b for b in (0,1). The vertical average averages the reader specific ROC curves along x = b for b in (0,1). The pooled average ignores readerID and pools all the scores together to create one ROC curve.

Examples

# Create a sample configuration file
config <- sim.gRoeMetz.config()
# Simulate an MRMC ROC data set
dFrame.imrmc <- sim.gRoeMetz(config)
# Analyze the MRMC ROC data and compute ROC curves
aucResult <- doAUCmrmc(dFrame.imrmc, flagROC = TRUE)

iMRMC documentation built on Sept. 11, 2024, 7:12 p.m.