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    $\begingroup$ Thanks, Robert. Yes that's exactly what I thought that fitting a slope with just one observation doesn't make sense, of course. Thats why I thought I should remove these subjects from my dataset, but your answer convinced me that I should stick to the full data set and just fit random intercepts. $\endgroup$ Commented Jul 2, 2020 at 19:19
  • $\begingroup$ @Kathrin Glad to hear it. It's much more important to retain your data than delete observations in order to fit a more complex model. $\endgroup$ Commented Jul 2, 2020 at 19:38
  • $\begingroup$ One more question: I just realised that I have more measurements- I removed the values at time zero because I added a variable for the baseline value. Is it allowed to use these measurements in zero although I use baseline as a covariate? in this case I wouldn't run into identifiability problems even when fitting a random slope. $\endgroup$ Commented Jul 3, 2020 at 18:48
  • $\begingroup$ @Kathrin that sounds like a good idea to me. In addition to helping identify random slopes, note that regressing follow-up on baseline is often a very dubious thing to do when analysing change. $\endgroup$ Commented Jul 4, 2020 at 19:08
  • $\begingroup$ Thank you once again! Sorry for all these questions but I'm really not sure... basically we are interested in the change from baseline to a later measurement so we also considered to model the change instead of the value itself. But I think I have to look for good sources providing more explanations on that. $\endgroup$ Commented Jul 4, 2020 at 20:02