How to assess confounding in negative binomial regression

[PaigeMiller]

Confounding usually refers to the X variables only. You can assess it using PROC REG (and many other procedures) with a fake continuous Y variable. VIF and similar measures are what you want.

[KKIND]

@PaigeMiller, thank you very much! What I am trying is running a code with the DV and the primary IV and then running it again adding the variable I am assessing for confounding and checking for the difference in estimates output table. Is difference more than 10% then the variable is a confounder. Does that make sense or do I just look at the p value by running the code  DV= IV and the variable, and if significant, consider it a confounder? Thanks!

[PaigeMiller]

---

@KKIND wrote:

@PaigeMiller, thank you very much! What I am trying is running a code with the DV and the primary IV and then running it again adding the variable I am assessing for confounding and checking for the difference in estimates output table. Is difference more than 10% then the variable is a confounder. Does that make sense or do I just look at the p value by running the code  DV= IV and the variable, and if significant, consider it a confounder? Thanks!

---

This should have been explained in your original message. Instead you make me guess at what you want, and waste my time.

I will stick with my original answer, I feel that is a better way to address confounding than your 10% method.

[KKIND]

@PaigeMiller, apologies for having wasted your time. Thanks again.