Interpreting a longitudinal generalized estimating equations beta cofficients 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 BloodPressure as CortisolStress increases.  This second part, the longitudinal part, is where I'm getting tripped up.
Am I understanding this correctly?
 A: The betas are very similar in interpretation to those from OLS, but for a population average. For a one unit increase in Cortisol Stress, you'd expect, on average, a 9.5 unit increase in blood pressure, holding all other variables constant. You can also interpret these as slopes associated with the predictor.
The time component in your model is just another controlling factor that you've added into your model. The beta associated with CortisolStress is the slope associated with CortisolStrees while holding time constant (chose any time frame you like -- so it's more akin to your "at any give time" interpretation) as well as the other independent variables in your model. It does not say anything about changes over time. If you wanted to say something about Weight and if/how it changes over time, you'd need to include an interaction term in your model: (i.e. + Time*Weight)
By the way you should not be interpreting GEE coefficients as relating to individuals -- GEE models are marginal models and so the conclusions you draw from them are population-based. See the following discussion I've commented on regarding interpretation:  Conditional vs. Marginal models
