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I'm running a cox regression to test the effect of different variables on crime.

There's one independent variable of interest (X), for which I hypothesize a negative relationship with crime (i.e., HR<1). This is usually what I get, also when I include several control variables. However, when I insert two specific independent variables (let's call them A and B) to the model together, I get a significant HR>1 for X. When I insert each one without the other (and with all other control variables), the coefficient of X remain <1. Any idea what could cause this?

I checked for multicollinearity and all VIF's are at least 0.7. I also tried to insert an interaction term between A and B, and it is significant, but the coefficient of X is still positive (HR>1).

Thanks!

Eran
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  • There might be nothing specific to a Cox model here. This type of thing happens in all sorts of regression models. See for example this page, or this page, or this page. As you don't (yet) show any examples of the results you are getting, it's hard to say much else. If you think that there's something specific to a survival model in your situation, then please edit the question to provide more details about what you've found so far. – EdM Sep 15 '23 at 16:58

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