Hi, assume that I have a repeated measure dataset with 3 time points: baseline, 
day 5 and day 10. There are 4 treatment groups (vehicle, treatment 1, treatment 
2 and treatment 3). 20 subjects per treatment group. A simple straight-forward 
way to analyze the data is to use mixed model:

model 1:
obj <- lmer(y ~ treatment * time +(time|subject)) where time is numeric with 
value 0,5 and 10.

The problem with this approach is that this model does not account for baseline 
imbalance between treatment groups. But if I want to include baseline value of 
the response variable in the model, then I think I have to exclude the baseline 
data from the rows of the dataset (so that baseline will become one variable, 
i.e. one column of the dataset, correct me if I am wrong). With this dataset 
tranformation, I only end up with 2 time points left in the dataset (day 5 and 
day 10). Then a linear term on the numeric time variable is not possible in 
lmer(). 


In this situation, what I can think of is to treatment time variable as  a 
factor (say named as "time.f"), and run the following model:

model 2:
obj<- lmer(y ~ treatment * time.f +(1|subject)) where time.f is a factor with 
value 5 and 10.

Couple of questions:

1. Should we really concern about the baseline imbalance by including baseline 
as a variable in the model? What's the advantage of doing so versus not doing 
so? 

2. If the objective of the study is to evaluate at the end of the study  (day 
10), which treatment group produces significantly difference from  the vehicle 
group, is model 2 a reasonable model to do that?
3. In general, with a repeated measures of 2 to 3 time points, is mixed models 
really necessary? In mixed-model mailing list, I realized that there is concerns 
about running mixed models on just a few time points. But I feel uncomfortable 
to run simple ANOVA (or ANCOVA) while completely ignore the fact the data 
arecorrelated among time points.
4. What are the better alternatives analyzing such datasets?

Thanks

John



      
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