Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
Frontal_Med_Lt ~ 1 + SUVmax_VAT + FU_Period + SUVmax_VAT * FU_Period +
(1 + FU_Period | pNo)
Data: brain
REML criterion at convergence: -164.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1931 -0.3939 0.0999 0.5484 2.2075
Random effects:
Groups Name Variance Std.Dev. Corr
pNo (Intercept) 1.869e-02 0.136722
FU_Period 2.429e-05 0.004928 0.22
Residual 2.153e-02 0.146743
Number of obs: 355, groups: pNo, 150
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.00790 0.06486 244.64322 15.539 < 2e-16 ***
SUVmax_VAT 0.08089 0.12188 244.70962 0.664 0.50752
FU_Period 0.02476 0.01272 91.09228 1.946 0.05471 .
SUVmax_VAT:FU_Period -0.08331 0.02662 104.47484 -3.130 0.00227 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SUVm_VAT FU_Prd
SUVmax_VAT -0.971
FU_Period -0.548 0.553
SUV_VAT:FU_ 0.465 -0.493 -0.969
This is the result of the LMM I fitted. As you can see, the interaction term is significant while the fixed effects are not. Can I say that SUVmax_VAT has an effect on the dependent variable? How do I interpret this result?