Repeated-measures analysis with count data following a negative-binomial distribution

Dear all,

I am looking for advice on how to run a repeated analysis on count data.

My issue is as follows:

  *   We counted the number of carotid plaques from ultrasound images in a
diseased population and a group of control subjects
  *   We have measured the plaques for all subjects in both the left and
the right carotid artery during the same session
  *   The number of plaques is a count score ranging from 0 to 6
  *   The distributions look like this:
     *   Plaques in the left carotid artery: https://www.dropbox.com/s/
t5tqh4wjrfc5eml/Left.png?dl=0
     *   Plaques in the right carotid artery: https://www.dropbox.com/s/
nl5ezef145av2ae/Right.png?dl=0
     *   (Where NKI= the diseased population; RSS= the control subjects;
(all)= the two groups combined. There are no numbers on the x-axis, but the
7 columns are the count scores 0-6 (left to right).)
  *   There is biological evidence that the distribution of plaques for
left and right differ in the general population (i.e., our control
subjects).
  *   I would like to test if the difference in distribution of plaque
scores between the left and right carotid arteries is different between my
two populations.
  *   Previous analyses (e.g., comparing a single side between groups)
showed me that a negative binomial distribution is a better fit for my data
than a Poisson distribution.

My idea is to run a repeated-measures negative binomial regression
analysis where plaque score measures (left and right) would be the repeated
measures. In this case I would be interested in the ?body side? by group
interaction.

My questions are:

  *   Is a good and valid approach?
  *   I am thinking about using R?s GEE package (
https://cran.r-project.org/web/packages/gee/index.html). Would that be
the right tool for this job?

Thanks!

- Vincent

        [[alternative HTML version deleted]]