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Re: st: Interpreting interactions


From   Rieza Soelaeman <rsoelaeman@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Interpreting interactions
Date   Wed, 17 Oct 2012 01:52:31 -0500

All--Sorry if you're receiving this twice, statalist bounced my last
response because I forgot to change my email settings to plain text.
rhs  see response below "---" if your email provider cuts off the msg

----------

Amal,
If your interaction term is statistically significant, then you must
interpret the main effect (e.g. i.mom_race2, marriedx) along with the
interaction term.  The purpose of having interactions is that you
suspect the effect of variable B to be different across different
levels of variable A.  Interpretation of dummy variables for race
remains the same, with the referent category as the one omitted.  The
only added complication here is that you have to say whether effect
differs by marital status, whereas w/o interaction, you're holding
marital status constant.

You should write out your interacted model for each mom_race2 category
then substitute 0s and 1s in the interaction (just for the
race/marital status dummies).  That way you can visualize the effect
of the interaction.

HTH,
Rieza


On Tue, Oct 16, 2012 at 3:44 PM, Amal Khanolkar <Amal.Khanolkar@ki.se> wrote:
> Hi,
>
> I ran the following set of logistic regression models:
>
> 1. Crude:
>
> xi: logit vent6h i.mom_race2 sexx if age_mom!=. & parity!=. & gestcalc!=. & cigs_befx!=. & gestdb!=. & gesthy!=. & MBMI!=. & ht_cm!=. & plural==1 & edu_mom!=. & marriedx!=., or
>
> 2.  Adjusted for confounders:
>
> xi: logit vent6h i.mom_race2 i.edu_mom i.marriedx age_mom sexx age_mom i.parity gestcalc i.cigs_befx i.gestdb i.gesthy i.MBMI ht_cm if plural==1., or
>
> 3. With interactions:
>
> xi: logit vent6h i.mom_race2*i.edu_mom i.mom_race2*i.marriedx sexx age_mom i.parity gestcalc i.cigs_befx i.gestdb i.gesthy i.MBMI ht_cm if plural==1., or
>
>
> I see that the Odds ratios (for racial groups) do not really change between models 1 and 2 - i.e. additional adjustment for potential confounders do not seen to affect the odds of a particular ethnic group of being diagnosed with my outcome of interest.
>
> However, I see that the OR for ethnic group 2 go from 1.23 (95% CI 1.00 to 1.48) in model 1 to 1.13 (0.92 to 1.37) in model 2 to 2.33 (1.46 to 3.72) in model 3.
>
> Model 3 only has the interactions in it, otherwise it is the same as model 2. How does one interpret the OR's for i.mom_race2 in model 3? I get the usual set of OR's for mom_race2 in the beginning of the output, and the the interactions towards the bottom of the model.
>
> I assumed that the OR's in model 3 should be same as those in model 2 as I'm not additionally adjusting for anything new (ie it is the same as model 2 except for the interaction term).
>
>
> Thanks,
>
>
>
> Amal Khanolkar
>
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