MRMCaov-package: MRMCaov: Multi-Reader Multi-Case Analysis of Variance

In MRMCaov: Multi-Reader Multi-Case Analysis of Variance

MRMCaov: Multi-Reader Multi-Case Analysis of Variance

Description

Estimation and comparison of the performances of diagnostic tests in multi-reader multi-case studies where true case statuses (or ground truths) are known and one or more readers provide test ratings for multiple cases. Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) doi: 10.1002/sim.2024. The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) doi: 10.1117/12.2549075.

Details

The functions below are available in MRMCaov for estimation and comparison of test performance metrics in studies involving multiple cases and one or more readers. Examples of their use can be found in the online guide at https://brian-j-smith.github.io/MRMCaov/.

Statistical Inference:

mrmc	Multi-reader multi-case ANOVA
srmc	Single-reader multi-case ANOVA
stmc	Single-test (single-reader) multi-case Estimation

Tabular and Graphical Summaries:

parameters	ROC curve parameters
plot	ROC curve plots
roc_curves	ROC curves
summary	Statistical analysis summaries

Performance Metrics (Binary Rating):

binary_sens	Sensitivity
binary_spec	Specificity

Performance Metrics (Ordinal or Numeric Rating):

binormal_auc	Binormal ROC AUC
binormal_sens	... sensitivity
binormal_spec	... specificity
binormalLR_auc	Binormal likelihood ratio ROC AUC
binormalLR_sens	... sensitivity
binormalLR_spec	... specificity
empirical_auc	Empirical ROC AUC
empirical_sens	... sensitivity
empirical_spec	... specificity
trapezoidal_auc	Empirical ROC AUC
trapezoidal_sens	... sensitivity
trapezoidal_spec	... sensitivity

Performance Metric Covariance Estimation Methods:

DeLong

jackknife

unbiased

ROC Curves:

roc_curves	Estimate one or more curves
parameters	Extract curve parameters
points	Extract curve points
mean	Compute the mean of multiple curves
plot	Plot curves

Conversion of MRMC Model Parameters:

OR_to_RMH	Obuchowski-Rockette to Roe, Metz & Hillis parameters
RMH_to_OR	Roe, Metz & Hillis to Obuchowski-Rockette parameters

Note

This research was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under Award Number R01EB025174

Author(s)

Maintainer: Brian J Smith [e-mail]

Authors:

• Stephen L Hillis [e-mail]

Other contributors:

• Lorenzo L Pesce [e-mail] [contributor]

See Also

Useful links:

• https://github.com/brian-j-smith/MRMCaov

• Report bugs at https://github.com/brian-j-smith/MRMCaov/issues

MRMCaov documentation built on Jan. 11, 2023, 1:14 a.m.