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I have a data set including four variables (ID,Time, group, result) in the following format. I want to see if the result (such as Calcium) change over time in different group or not. I was wondering what is the right statistical model to use? Can I use two way mixed anova for this analysis?

> Id     time   group     Result 
  1        0       0      
  1        1       0
  1        2       0
  2        0       1
  2        1       1
  2        2       1
  3        0       0
  3        1       0
  3        2       0    

Also, I am confused between choosing aov with Error(ID/Time) and lmer with (Time|ID)? Which one I need to choose?

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In case you have a balanced design in which all subjects only have measurements on specific time points, and provided that you have a moderate number of subjects, the best you could do is to fit a marginal model with an unstructured covariance matrix, e.g., using function gls() from package nlme. In your case, something like

fm <- gls(Result ~ time * group, data = <your_data>, correlation = corSymm(form = ~ 1 | Id), weights = varIdent(form = ~ 1 | time))

summary(fm)
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  • $\begingroup$ What is the required number of repeats in order to use gls? Forexample, can I use it if I have more than 3 time points in my data set? Also do I need to check the Sphericity assumption if I want to apply your suggestion? $\endgroup$ – stat Aug 31 '18 at 13:38

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