Hi,
I have two different imaging modalities (for the identification of areas of
infarcted myocardium) that I need to compare regarding agreement and
consistency.
However, I don't think that methods like Cohen's Kappa, PCC,
Bland-Altmann
or ICC are sufficient here as there is not just a pairwise but also a
spatial relationship between measured data points. For example if the
results of the two imaging modalities are slightly misaligned their
agreement might be still much better than the mentioned tests might
indicate (at least from a practical point of view). On the other hand the
number of non-infarcted myocardial segments often heavily outweighs the
number of infarcted segments which seems to introduce a bias towards better
agreement in most examinations.
Here is an example data set, where I've calculated the ICC.
What would be the "correct" way to assess agreement and consistency
here?
# modality 1
lgemtx <- as.matrix(read.table('
http://cluster010.ovh.net/~myvideoc/R20130305/lge_mtx.csv'))
# modality 2
petmtx <- as.matrix(read.table('
http://cluster010.ovh.net/~myvideoc/R20130305/pet_mtx.csv'))
# ICC
# note: modalities are inverse
# thus in modality1 "0" denotes a normal segment and "100" a
complete
infarction of the segment
# and vice versa in modality2
library(irr)
print(icc(cbind(100 - c(pet_mtx), c(lge_mtx)), model="twoway",
type="c"))
print(icc(cbind(100 - c(pet_mtx), c(lge_mtx)), model="twoway",
type="a"))
If the matrices are mapped to polar plots (also called bullseye plots) one
can visually assess their agreement.
Each matrix row defines one circle starting at 0° with columns equally
mapped to segments of 3,6° width.
The first row defines the outmost circle, the last row defines the inner
circle.
Here are the corresponding plots for the matrices given above (red is
infarction).
http://cluster010.ovh.net/~myvideoc/R20130305/LGE.png
http://cluster010.ovh.net/~myvideoc/R20130305/PET.png
I know, this is not a pure R question but more a general statistical one.
Hopefully it is still ok to post it here :-)
Best, Felix
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