st: Appropriate modelling - testing which set of exposures are more important

[Amal Khanolkar <Amal.Khanolkar@ki.se>]

Hello all,

I need some advice on the following approach:

I have two main exposures; maternal ethnicity and maternal socioeconomic position (SEP).

I want to test which of the above two exposures are more important in determining maternal pregnancy outcomes.

1. I plan to use linear regression, as my outcome of interest is continuous. 
2. Initially, the first model will test the effect of ethnicity on the outcome, controlling for potential confounders as follows:

xi: regress outcome i.ethnicity confounder1 confounder2 i.confounder3

3. In the next step, I introduce the second main exposure, maternal SEP:

xi: regress outcome i.ethnicity confounder1 confounder2 i.confounder3 i.SEP

4. I test for an interaction as follows:

xi: regress outcome i.ethnicity*i.SEP confounder1 confounder2 i.confounder3 

Questions: If the effects of ethnicity on my outcome of interest change from step2 to step3, controlling for the same confounders in both models, is this enough evidence of one exposure being more important than the other? (I assume, this isn't completely right, as in essense the model in step 3 is the effect of SEP on the otcome adjusting for ethnicity). But I hope the model with the interaction test solves this to some extent, as I will be able to see if socieconomically disadvantaged mothers of certain ethnicites  have a worse outcome compared to other disadvantaged mothers belonging to other ethnic groups.

If there are any better ways to improve the above approache - please let me know.

Regards,

/Amal.
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