I am interested in examining the effect of time on the levels of antibodies and how that relationship in influenced by the interaction of gender and age. This is how I have built my model. I have included a random intercept for repeated measures.
model <- lmer(Antibody ~ Time + (Sex*Age)+ (1|PID), data=metadata)
This is the result
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Antibody ~ Time + (Sex * Age) + (1 | PID)
Data: metadata
AIC BIC logLik deviance df.resid
441.0 461.3 -213.5 427.0 128
Scaled residuals:
Min 1Q Median 3Q Max
-2.14160 -0.28264 -0.01453 0.26966 2.27572
Random effects:
Groups Name Variance Std.Dev.
PID (Intercept) 1.8039 1.343
Residual 0.3003 0.548
Number of obs: 135, groups: PID, 95
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.717567 0.263584 134.327633 25.485 < 2e-16 ***
Time -0.013952 0.002131 54.645901 -6.547 2.13e-08 ***
Sex 0.740442 0.299279 92.341423 2.474 0.0152 *
Age -0.101281 0.227621 92.276736 -0.445 0.6574
Sex.Male:Age 0.655921 0.307147 91.570395 2.136 0.0354 *
How can I interpret this output? Does the significant interaction between males and age mean that older males have higher antibodies for longer? If not how should I formulate the model to test for the impact of the interaction of age and sex on the duration of the antibody?
Thanks in advance