How to conduct a multilevel (hierarchical) binary logistic regression (in SPSS, R, or other software)? I have a model with occurrence of a disease represented by a binary dependent variable (DV)  and 8 independent variables (IVs) at different levels. I need to create a multi-level model, in which the treatment is placed in the lower order and the demographics are in the higher order.
However, I am not familiar with the multilevel model for logistic regression. Please give me some names of necessary multilevel analyses for doing a multilevel binary logistic regression (and any hints you think are useful). I wonder if GEE (generalized estimating equation) is the answer, because I have correlations between the IVs? What about "conditional (fixed-effects) binary logistic regression" again for the paired data among my IVs? Or else? ...
 A: R allows what are called generalized linear mixed effects models. In these, the response variable is allowed to be from a few different families, including binomial (which, if coded as 0 and 1, gives logistic regression). 
The function used to be called glmer(). I'm pretty sure that now more recent versions of the regular mixed effects models function lmer() allows you to specify a family (e.g. 'binomial') and a link function (e.g. 'logit'). lmer() allows the specification of random effects and nesting. You can find more info on Doug Bates' slides, in particular the very last one,  here . He wrote lmer(), so I believe him when he says it works.
Keep in mind that you need numerous (more than 6 or so) different 'subjects' to be able to estimate random effects efficiently.
A: Is this question related to your question here? Do you want to assess the effect of treatment on disease, with matched (paired) individuals?
Anyway, the difference between conditional logistic regression and GEE is the interpretation. If you want to get subject specific estimate, you can use conditional logistic regression (e.g. clogit in R), otherwise for population average estimate, you can use GEE (e.g. R package gee). Note that the reason to use multilevel models is the correlation within paired data.
