# Lmer set up for repeated measurements?

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

Random effects: Groups Name Variance Std.Dev. regions:ID (Intercept) 0.06566 0.2562
ID (Intercept) 0.01917 0.1385
Residual 0.01506 0.1227

Fixed effects: 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: (Intr) thickness thickness -0.661
timepoint1 0.017 -0.026

1. What is the intercept for fixed effects?
2. How can I answer if there was a significant increase or decrease in volume from time point 1 to timepoint 2?
3. Can I obtain regional effects? i.e. Region 12 increased from timepoint 1 to time point 2 ? Proposed Model:
MODEL2 = lmer(volume~ thick + timepoint + regions + (1|ID/regions), data = DATA )

lmer(volume ~ time + thickness + (1 | ID / regions))

where time` is is binary variable taking the value 0 for the first time point, and 1 for the second one.
• Thank you for your reply! I have time as a factor 1, 2. Is it necessary to make it binary 0,1 ? data.frame': 8288 obs. of 5 variables: $volume : num 579 951 229 286 844 ...$ thick : num 2.37 2.09 1.94 2.6 2.78 ... $timepoint: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...$ regions : Factor w/ 148 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ... \$ ID : Factor w/ 139 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ... – Sheraz Jan 6 '19 at 21:24