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Posted 08-10-2015 03:29 PM
(3668 views)

Does anyone have any resource on the estimates or process for looking for evidence for confounding in proc logistic? I know this is a stats question, but any leads/suggestions, resources would be helpful for interpreting the proc logistic output for confounders.

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PROC CORR.

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Thanks for your reply @dcruik. I see what you mean @philc. Yes, that is part of what I am looking for. I did come across your idea on the internet but the process wasn't clear. So just to understand it better...this is my model for proc logistic regression...

proc logistic data=lr;

class.....etc

model z= a b c d e f g h;

run;

g and h are control variables. Also, all the variables in the model are dummy variables.. Its still a numeric variable with a discrete outcome? So I run a proc reg only on the control variables to i.e.

proc reg data=lr;

model z= g h /vif;

run;

The reason I am asking is because I have already run chisq to look for significance of association for the other predictor variables.

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Looking at a correlation matrix is also advised by Gareth James, et al. "An Introduction to Statistical Learning". Collinearity is discussed starting around page 113.

If you are going to use VIF or correlation matrices, you want to consider all of your independent variables. The use of the word independence is meaningful because this confounding is typically because the independent variables are not truly independent of each other, yet true independence is an assumption that is assumed to be true when one performs any linear regression.

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Thanks @dcruik and @PhilC. I did realize I have to include all the independent variables in for the VIF. Just couldn't get back yesterday to add a comment The explanations make sense @PhilC. I wanted something very precise to help me in my decision. This helped a lot. I will refer to the books suggested,

Best,

D R.

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