Repeated measure t test with covariates in R I have a two data-sets of a set of subjects with values for their baseline and followup visit. I would like to do a repeated measure test to see whether there is a significant difference between the two sets (baseline & followup). I know I can do a simple paired t-test. But I need to adjust my values for covariates like age, etc...
I would like to perform a GLM method (if possible) to see whether there is a significant difference between the two sets with the covariate adjustments. Please advice how can I do this in R.
 A: I gather you have only 2 repeated measures.  That makes this simpler.  If you had >2, you would need to use a mixed effects model, which is more advanced.  Given that you have only 2, there are two basic possibilities: use differences as your response variable, or use an ANCOVA.  Which you should use has traditionally been a matter of great contention in statistics.  A basic rule is that ANCOVA makes more sense if you believe your groups were the same at baseline (i.e., this is an experiment), and using differences as your response variable makes more sense if you don't have reason to believe the groups were the same (i.e., this is an observational study).  For more on this topic, read: Best practice when analysing pre-post treatment-control designs.  
If you choose to use differences as your response variable, the approach is quite simple.  You just subtract the baseline value for each subject from the subject's followup value.  Then use those differences as Y in a multiple regression model.  In R it might be something like:  
difs = followup-baseline
lm(difs~covariates)  

If you choose to use ANCOVA, then you use the followup value as the response variable and include the baseline value as on of your covariates.  In R it might be something like:  
lm(followup~covariates)  # ("covariates" includes "baseline" & the original covariates)

