Mixed-model-binary logistic model with dependence between individual repeated measures

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<snip />
 I do not
want to assume that. In addition I would like to be able to chose
other distributions than the normal for my random effect, which is
not possible in SAS (proc NLMIXED). 
  It's not possible in R either as far as I know.
I was reading the article in the latest issue of the R Journal on the
hglm package, and although I was only giving it a cursory scan over
lunch it looks like it might be able to fit the sort of model implied
here; random effects distributed as a member of the exponential family.

G
The generalized estimating
equation packages are probably not an option as I do not whant
marginal models. I will look at the references you suggested. Thank
you. /Anna

  If you want a non-marginal model with non-normal random effects and
within-individual correlation structures other than compound symmetry
(i.e. simple block structures), you are probably going to have to
construct your own solution with WinBUGS or AD Model Builder or ... ? If
you're lucky, MCMCglmm may be able to do what you want -- I would check
it out. (Molenbergh and Verbeke's book on longitudinal models describes
approaches for non-normal random effects, but in the context of LMMs
(i.e. normally distributed errors) -- they may have done something to
extend this stuff to GLMMs more recently.  It's possible that someone
out there has done what you want and encapsulated it into a canned
package, but I doubt it.

   cheers
    Ben Bolker
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Mixed-model-binary logistic model with dependence between individual repeated measures

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