I have 139 subjects (ID), with measurements taken at two time points (Time1, Time2), at 148 brain regions, a dependent measure called volume, and a covariate called thickness.
Each subject has 148 brain regions with volume and thickness measured twice
I am trying to find out if there is a difference in volume between timepoint 1 and timepoint 2 while controlling for thickness. I want to know which brain regions show this difference. I need help setting up the model. Specifically the timepoint part is throwing me off...
I am using R. and trying to figure out a model with linear mixed models with (1|ID) as random factor, fixed factors regions, thickness.
What I have is:
delta-volume <- volume_time 2 - volume_time1
Mod1 lmer(delta-volume ~ delta.thickness + volume_time_1 + regions + (1|ID))
Mod2 lmer(delta-volume ~ delta.thickness + regions + (1|ID)
Mod3 lmer(delta-volume ~ delta.thickness + (1|ID/regions)
from model 1
1) is there a difference in volume from from timepoint 1 to timepoint 2?
2) Which regions show a significant difference?
The way I interpreted is for question 1) is that the fixed effect intercept is the value of the depedent variable when the IV variables are defaults so, in my case, the intercept was positive, I interpreted it as there was a decrease in volume. Is this correct?
2) stuck on how to see which regions....I get estimates for them but what do those mean?
Also would very much appreciate an explanation about how the models differ... Thank you!