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I want to run a linear regression analysis on my multiple imputed data. I imputed my dataset using mice. The formula I used to run a linear regression on my whole imputed set is as follows:

 mod1 <-with(imp, lm(outc ~ age + sex))
 pool_mod1 <- pool(mod1)
 summary(pool_mod1)

This works fine. Now I want to create a subset of BMI, by saying: I want to apply this regression analysis to the group of people with a BMI below 30 and to the group of people with a BMI above or equal to 30. I tried to do the following:

 mod2 <-with(imp, lm(outc ~ age + sex), subset=(bmi<30))
 pool_mod2 <- pool(mod2)
 summary(pool_mod2)

 mod3 <-with(imp, lm(outc ~ age + sex), subset=(bmi>=30))
 pool_mod3 <- pool(mod3)
 summary(pool_mod3)

I do not get an error, but the problem is: all three analysis give me exactly the same results. I thought this could be just the real life situation, however, if I use variables other than bmi (like blood pressure < 150), the same thing happens to me.

So my question is: how can I do subset analysis in R when the data is imputed using mice?

(BMI is imputed as well, I do not know if that is a problem?)

slamballais
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    Welcome to StackOverflow. When posting a question, please provide a [minimum reproducible example](https://stackoverflow.com/a/5963610/2359523) to aid others in answering your question. – Anonymous coward Oct 19 '18 at 19:02
  • Thank you, I will do this next time! –  Oct 20 '18 at 17:42

1 Answers1

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You should place subset within lm(), not outside of it. 

with(imp, lm(outc ~ age + sex, subset=(bmi<30)))

A reproducible example.

with(mtcars, lm(mpg ~ disp + hp)) # Both produce the same
with(mtcars, lm(mpg ~ disp + hp), subset=(cyl < 6))    

Coefficients:
(Intercept)         disp           hp  
   30.73590     -0.03035     -0.02484  


with(mtcars, lm(mpg ~ disp + hp, subset=(cyl < 6))) # Calculates on the subset

Coefficients:
(Intercept)         disp           hp  
   43.04006     -0.11954     -0.04609
Anonymous coward
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