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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 )
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Since you are interested in differences in volume between the two time points, you would need to include the time variable as a fixed effect as well, i.e.,

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.

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  • $\begingroup$ 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 ... $\endgroup$
    – Sheraz
    Jan 6 '19 at 21:24
  • $\begingroup$ No, if you have it as factor it will also work. $\endgroup$ Jan 6 '19 at 21:25
  • $\begingroup$ I edited my post with follow up. I am confused what the fixed effects intercept means here? Added some questions. Thank you so much for your help $\endgroup$
    – Sheraz
    Jan 6 '19 at 21:43

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