Hi,

I am a student, starting to deal with linear mixed models in STATA and your help would be extremely valuable.



1. I wonder whether or not I need to add a random slope in addition to the random intercept in linear mixed models. I am studying the impact of hypertension on cognition over time.

xtmixed outcome predictor covariates time || id: time OR xtmixed outcome predictor covariates time || id:

Using the model with the lowest AIC is a good option? Do I need to systematically add a random slope when I work on repeated data over time with linear mixed models?



2. I wanted to confirm is my interpretation is correct for the following outputs:

Code:
 xtmixed cognition blood_pressure year || id : year
blood_pressure: coeff = -0.18 et p=0.24
year: coeff = -0.47 et p<0.001
_cons: 90.78

In this case, I understand that whatever the time during follow-up, a subject with 1 mmHg increase in blood pressure will have -0.18 of cognition. I also understand that for each additional year, a subject will have -0.47 points of cognition. And the difference of cognition between a subject with 130 mmHg of blood pressure and 140 mmHg will stay the same all the time = 1.8 point

Code:
 xtmixed c.cognition blood_pressure##c.year || id : year
blood_pressure: coeff = -0.32 et p=0.04
year: coeff = -0.47 et p<0.001
c.blood_pressure#c.year: coeff = 0.10 et p<0.001
_cons : 90.78

In this case, I understand that for each additional mmHg of blood pressure, cognition will be lower (-0.32). For each additional year, also lower (-0.47). But the impact of hypertension on cognition is not the same during the follow-up. I'm not able to calculate the difference of cognition between a patient with 130 and another one with 140 mmHg of blood pressure at year 2 for example and the same thing at year 4.

How can I compute those differences with STATA?

Lincom, margins?

Thank you very much for your help. I'm a medical student trying to deal with linear mixed models for the first time without help