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.
I was thinking
lmer(volume ~ thickness + (1 | ID / regions)?
EDIT: lmer(volume ~ thickness + timepoint + (1 | ID / regions)`
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest'] Formula: volume ~ thickness + timepoint + (1 | ID/regions) Data: DATA
REML criterion at convergence: -1704.6
Scaled residuals: Min 1Q Median 3Q Max -6.5771 -0.2711 -0.0559 0.1816 9.6790
Groups Name Variance Std.Dev.
regions:ID (Intercept) 0.06566 0.2562
ID (Intercept) 0.01917 0.1385
Residual 0.01506 0.1227
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.247e-02 3.533e-02 8.500e+01 2.617 0.0105
thickness 1.449e-01 9.615e-03 7.607e+03 15.068 <2e-16
timepoint1 -1.320e-02 1.349e-03 4.086e+03 -9.787 <2e-16
Correlation of Fixed Effects:
timepoint1 0.017 -0.026
- What is the intercept for fixed effects?
- How can I answer if there was a significant increase or decrease in volume from time point 1 to timepoint 2?
- Can I obtain regional effects? i.e. Region 12 increased from timepoint 1 to time point 2 ?
MODEL2 = lmer(volume~ thick + timepoint + regions + (1|ID/regions), data = DATA )