sim.gRoeMetz: Simulate an MRMC data set of an ROC experiment comparing two...

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

Simulate an MRMC data set of an ROC experiment comparing two modalities

Description

This procedure simulates an MRMC data set of an ROC experiment comparing two modalities. It is based on Gallas2014_J-Med-Img_v1p031006, which generalizes of the model in Roe1997_Acad-Radiol_v4p298 and Roe1997_Acad-Radiol_v4p587. Specifically, it allows the variance components to depend on the truth and the modality. For the simpler Roe and Metz model, you can enter the smaller set of parameters into sim.gRoeMetz.config and it will return a larger set of parameters that can be used with this function.

Usage

sim.gRoeMetz(config)

Arguments

config

[list] of simulation parameters: Experiment labels and size modalityID.A: [character] label modality A modalityID.B: [character] label modality B nR: [numeric] number of readers nC.neg: [numeric] number of signal-absent cases nC.pos: [numeric] number of signal-present cases There are six fixed effects: mu.neg: [numeric] signal-absent (neg, global mean) mu.pos: [numeric] signal-present (pos, global mean) mu.Aneg: [numeric] modality A signal-absent (Aneg, modality effect) mu.Bneg: [numeric] modality B signal-absent (Bneg, modality effect) mu.Apos: [numeric] modality A signal-present (Apos, modality effect) mu.Bpos: [numeric] modality B signal-present (Bpos, modality effect) There are six random effects that are independent of modality var_r.neg: [numeric] variance of random reader effect var_c.neg: [numeric] variance of random case effect var_rc.neg: [numeric] variance of random reader by case effect var_r.pos: [numeric] variance of random reader effect var_c.pos: [numeric] variance of random case effect var_rc.pos: [numeric] variance of random reader by case effect There are six random effects that are specific to modality A var_r.Aneg: [numeric] variance of random reader effect var_c.Aneg: [numeric] variance of random case effect var_rc.Aneg: [numeric] variance of random reader by case effect var_r.Apos: [numeric] variance of random reader effect var_c.Apos: [numeric] variance of random case effect var_rc.Apos: [numeric] variance of randome reader by case effect There are six random effects that are specific to modality B var_r.Bneg: [numeric] variance of random reader effect var_c.Bneg: [numeric] variance of random case effect var_rc.Bneg: [numeric] variance of random reader by case effect var_r.Bpos: [numeric] variance of random reader effect var_c.Bpos: [numeric] variance of random case effect var_rc.Bpos: [numeric] variance of randome reader by case effect

Details

The simulation is a linear model with six fixed effects related to modality and truth and 18 normally distributed independent random effects for readers, cases, and the interaction between the two. Here is the linear model:

L.mrct = mu.t + mu.mt
+ reader.rt + case.ct + readerXcase.rct
+ modalityXreader.mrt + modalityXcase.mct + modalityXreaderXcase.mrct

• m=modality (levels: A and b)

• t=truth (levels: neg and Pos)

• mu.t is the global mean for t=neg and t=pos cases

• mu.mt is the modality specific fixed effects for t=neg and t=pos cases

• the remaining terms are the random effects: all independent normal random variables

Value

dFrame.imrmc [data.frame] with (nC.neg + nC.pos)*(nR+1) rows including

• readerID: [factor] w/ nR levels "reader1", "reader2", ...

• caseID: [factor] w/ nC levels "case1", "case2", ...

• modalityID: [factor] w/ 1 level config$modalityID

• score: [numeric] reader score

Note that the first nC.neg + nC.pos rows specify the truth labels for each case. For these rows, the readerID must be "truth" and the score must be 0 for negative cases and 1 for positive cases.

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