I have a random intercept logistic regression (due to repeated measurements) and I would like to do some diagnostics, specifically concerning outliers and influential observations.
I looked at residuals to see if there are observations that stand out. But I would also like to look at something like Cook's distance or DFFITS. Hosmer and Lemeshow (2000) say that due to the lack of model diagnostic tools for correlated data, one should just fit a regular logistic regression model ignoring the correlation and use the diagnostics tools available for regular logistic regression. They argue that this would be better than doing no diagnostics at all.
The book is from 2000 and I wonder if there are methods available now for model diagnostics with mixed effects logistic regression? What would be a good approach to check for outliers?
Edit (Nov 5, 2013):
Due to the lack of responses, I am wondering if doing diagnostics with mixed models is not done in general or rather not an important step when modeling data. So let me rephrase my question: What do you do once you found a "good" regression model?