convertDFtoDesignMatrix

Convert an MRMC data frame to a design matrix, dropping readers or cases with no observations

This software does 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. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.

Brandon Gallas

iMRMC

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

convertDFtoDesignMatrix function

<dl><dt>dfMRMC</dt>
<dd>An MRMC data frame</dd>
<dt>modality</dt>
<dd>The score matrix depends on the modality.
If more than one modality exists in the data frame,
you must specify which modality to subset.</dd>
<dt>dropFlag</dt>
<dd>[logical] The default setting (TRUE) removes readers and cases
that have no observations. Dropping them by default will speed up analyses.
Leaving the levels (dropFlag = FALSE) is useful if you need the entire score
or design matrix when comparing or doing analyses with two modalities.</dd></dl>

Arguments

Convert an MRMC data frame to a design matrix — convertDFtoDesignMatrix

<dl>

<dt>dfMRMC</dt>
<dd>An MRMC data frame</dd>


<dt>modality</dt>
<dd>The score matrix depends on the modality.
If more than one modality exists in the data frame,
you must specify which modality to subset.</dd>


<dt>dropFlag</dt>
<dd>[logical] The default setting (TRUE) removes readers and cases
that have no observations. Dropping them by default will speed up analyses.
Leaving the levels (dropFlag = FALSE) is useful if you need the entire score
or design matrix when comparing or doing analyses with two modalities.</dd>

</dl>

convertDFtoDesignMatrix: Convert an MRMC data frame to a design matrix

Description

Usage

Value

Arguments