- Thread starter klo
- Start date
- Tags age-adjusted means

Here is the example: i have 5 groups (ie. quantiles), and I want to know the mean of each variable (i.e. glucose, cholesterol etc.). Is easy to calculate means of each variable, but I want to be age-adjusted since want to compare the groups.

What I did is created a regression model: fit <- lm(glucose ~ age)

Now I dont know what to do to get age-adjusted mean for glucose.

Thank you.

Code:

`lm(glucose ~ 0 + group + age)`

Make sure that "group" is stored as a

Ok, i agree. But when i used formula above for systolic blood pressure, adjusted age mean was 100 mmHg, and crude mean 143 mmHg. This should not be correct.

Typically these age-adjusted means are mainly to provide a way to compare the groups at a common level - what you should be doing with them is comparing these numbers between groups. If the groups differ a lot in terms of age AND age does have a decent effect on the response then yes the age-adjusted means will be quite different from the raw means. If this is an issue for you then you really need to put more thought into what you actually want.