I was looking at this page and noticed the methods for confidence intervals for lme and lmer in R. For those who don't know R, those are functions for generating mixed effects or multi-level models. If I have fixed effects in something like a repeated measures design what would a confidence interval around the predicted value (similar to mean) mean? I can understand that for an effect you can have a reasonable confidence interval but it seems to me a confidence interval around a predicted mean in such designs seems to be impossible. It could either be very large to acknowledge the fact that the random variable contributes to uncertainty in the estimate, but in that case it wouldn't be useful at all in an inferential sense comparing across values. Or, it would have to be small enough to use inferentially but useless as an estimate of the quality of the mean (predicted) value that you could find in the population.
Am I missing something here or is my analysis of the situation correct?... [and probably a justification for why it isn't implemented in lmer (but easy to get in SAS). :)]