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I have a multiple regression where I have been asked to consider two exposure measures (say X1 and X2) and outcome Y.

The overall aim of the analysis is to examine the evidence for an association between Y and the two exposure measures using regression methods.

There are four potential confounders (C1, C2, C3, C4).

How can I check that the adjustment of the potential confounders affect the associations between the exposures of interest and Y.

Charlotte123
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  • Welcome to the site. How many observations do you have? If you have enough, you can just include them all in the model. – Frans Rodenburg Oct 29 '19 at 07:39
  • Thank you @FransRodenburg I have 239 observations. My understanding is that if I had only one exposure measure, I would run a simple linear regression, and then run another regression adjusted with a potential confounder to see if there has been at least 10% change. How do I check for confounding if I have two exposure measures? Thank you. – Charlotte123 Oct 29 '19 at 07:46
  • Do you run into any troubles running lm(Y ~ X1 + X2 + C1 + C2 + C3 + C4)? If you include potential confounders in the model, they do not confound the effects of X1 and X2 on Y. – Frans Rodenburg Oct 29 '19 at 07:48
  • @FransRodenburg It's possible that not all of them confound the effects of X1 and X2 with Y. I'm unsure what to look for. Thanks. – Charlotte123 Oct 29 '19 at 07:58
  • I'd say you have more than enough samples to include them all. Sure, including a nuisance variable that does not actually confound the effect of interest does not improve the estimation of the effect of interest, but unless they are mediators, there is little harm in including them (but much to be gained by preventing potential confounding). – Frans Rodenburg Oct 29 '19 at 08:09
  • Related: https://stats.stackexchange.com/a/314648/176202 – Frans Rodenburg Oct 29 '19 at 08:11

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