I've been struggling with wrapping my head around the GEE beta coefficients and I don't think I fully get it. There are other questions on CrossValidated that ask about GEE in the binary context (Generalized estimating equations output in SPSS and Interpretation of GEE coefficients) and while helpful do not help me understand continuous variables in a longitudinal setting.
Here is an example longitudinal analysis:
geeglm(BloodPressure ~ CortisolStress + Time + Sex + Age + Weight, id = SubjectID, family = gaussian, corstr = 'ar1', data = dataset1)
The data are sorted as:
> Time, SubjectID, ... > 1, 100, ... > 2, 100, ... > 3, 100, ... > 1, 101, ... > 2, 101, ... > ...
The beta coefficient for the
CortisolStress is, e.g., 9.50 (SE = 2.3). From what I understand, that means for individuals with a one unit increase in
CortisolStress have at any given timepoint a 9.50 unit increase in
BloodPressure and that there is a 9.50 unit increase in
BloodPressure averaged over time (longitudinal interpretation). Or said another way, as time increases there is a 9.50 unit increase in
CortisolStress increases. This second part, the longitudinal part, is where I'm getting tripped up.
Am I understanding this correctly?