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08-10-2015 03:29 PM

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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08-10-2015
04:12 PM

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08-10-2015 04:12 PM

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08-10-2015 03:41 PM

PROC CORR.

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08-10-2015 03:51 PM

I have categorical variables. proc corr will tell me about the linear relationship between 2 numeric variables. I have already sorted out measures of association using chisq.

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08-10-2015 04:03 PM

right... So logistic regressions do not use categorical variables either, so the categorical variables are converted to dummy variables... I don't know something that can make this easy. I must defer to another community member. It would be great if you could make a dataset that contains the dummy variables created by PROC LOGISTIC.

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08-10-2015 04:08 PM

Yes, you are right. I did convert the categorical variables to dummy variables. let me see if I can get the dataset with the dummy variables...

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08-10-2015
04:12 PM

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08-10-2015 04:12 PM

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08-10-2015 04:42 PM

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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08-11-2015 08:53 AM

I would recommend checking out this book by Paul Allison that discusses Logistic Regression using SAS. In Chapter 3.5, he discusses about Multicollinearity and checking the diagnostics using this proc reg with the vif option. There's an example that he uses that could help bring to light the purpose of what you're trying to do, and how to apply it with your data. Hope this can be a better reference.

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08-11-2015 10:15 AM

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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08-11-2015 12:27 PM

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.