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st: Adjusted Proportions
From
[email protected]
To
[email protected]
Subject
st: Adjusted Proportions
Date
Sun, 15 May 2011 17:01:17 -0700
Dear All:
Any help you can provide on the following would be sincerely
appreciated. I am using complex survey data and, fortunately, have
had luck obtaining adjusted means for age for each level of a frailty
variable (i.e., 1 = frail, 2 = pre-frail, and 3 = not frail) using the
following commands:
svy: regress age sex race smoker2 school2 income2 bmicat frailtycat
adjust sex race smoker2 school2 income2 bmicat, by(frailtycat) se ci
Then to get p-values I ran the following command:
xi:svy: regress age i.frailycat sex race smoker2 school2 income2 bmicat
testparm _Ifr*
Now for my question. I am having difficulty obtaining adjusted
proportions for categorical variables, specifically for different BMI
categories (1 = underweight, 2 = normal weight, 3 = overweight, and 4
= obese) across the different frailty groups.
Using the following glm command I was able to get the mean BMI
category value for each level of the frailty variable.
xi:svy: glm bmicat i.frailtycat sex race smoker2 school2 income2
adjust sex race smoker2 school2 income2, by(frailtycat) se ci
While adjusting for various covariates, however, I am trying to see
how many people are in different BMI categories (1 = underweight, 2 =
normal weight, 3 = overweight, and 4 = obese) across three different
frailty groups (1 = frail individuals, 2 = approaching frailness, and
3 = not frail).
Any help you can provide on obtaining adjusted proportions for this
situation would greatly appreciated.
Thanks and Kind Regards,
Paul
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