Hello,
My project is looking at factors associated with the slope in a repeatedly measured outcome. I used a mixed effect model with random slope. To specify that I wanted to look at factors associated with the outcome's slope, I created new variables using gen var_time= var*time for each factor (e.g. bmicat_time below). I coded the following which looks at whether BMI category influences the slope of the outcome:
xtmixed outcomevar bmicat_time time|| id: time
But I only get one estimate despite there being 4 categories of BMI. This is assuming linear trend of effect on the slope of outcomevar across BMI categories. I prefer this but my supervisor doesn't like it. Using i.bmicat_time gives me 30 estimates (i.e. 30 parameters) which I do not want. Is there a way to get 3 estimates which compare the slope of outcomevar in BMI categories 2, 3 and 4 with the slope of outcomevar in category 1.
My project is looking at factors associated with the slope in a repeatedly measured outcome. I used a mixed effect model with random slope. To specify that I wanted to look at factors associated with the outcome's slope, I created new variables using gen var_time= var*time for each factor (e.g. bmicat_time below). I coded the following which looks at whether BMI category influences the slope of the outcome:
xtmixed outcomevar bmicat_time time|| id: time
But I only get one estimate despite there being 4 categories of BMI. This is assuming linear trend of effect on the slope of outcomevar across BMI categories. I prefer this but my supervisor doesn't like it. Using i.bmicat_time gives me 30 estimates (i.e. 30 parameters) which I do not want. Is there a way to get 3 estimates which compare the slope of outcomevar in BMI categories 2, 3 and 4 with the slope of outcomevar in category 1.
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